Description:TECHNICAL FIELD
[0001] The present disclosure relates generally to the field of multi-modal data fusion systems and methods. More particularly, the present disclosure relates to a system and method for real-time fusion of heterogeneous sensor data using adaptive quantum-inspired processing elements for intelligence extraction across diverse sensing modalities.

BACKGROUND
[0002] Multi-modal sensor fusion represents a critical technological capability across domains including defense, healthcare, autonomous systems, and environmental monitoring. Existing approaches typically rely on conventional von Neumann computing architectures to process sensory data sequentially and often struggle with real-time integration of heterogeneous data streams characterized by different sampling rates, reliability metrics, and information densities.
[0003] The existing multi-sensor fusion systems typically rely on either centralized architecture vulnerable to communication bottlenecks and single points of failure, or distributed architectures, which often struggle to integrate a coherent global context. Moreover, existing systems typically rely on static processing pipelines, lacking flexibility to adapt in real time to changing environmental conditions, fluctuating sensor reliability, or evolving mission objectives. Such inflexibility limits responsiveness and resilience, especially in dynamic, real-time, or mission-critical environments.
[0004] Neural-inspired computing shows strong potential for sensor fusion due to its efficiency and adaptability, but current implementations are mostly software-based or use rigid, specialized hardware which are not capable of handling diverse sensor types. Further, quantum computing offers theoretical advantages for complex fusion tasks. However, current quantum hardware may not yet be practical for real-world deployment. As such, there is a clear need for a flexible, scalable, and adaptive computing architecture capable of supporting real-time, heterogeneous sensor fusion in dynamic environments.
[0005] Therefore, there exists a need for a novel hardware-software architecture to above-mentioned drawbacks and limitations while enabling energy-efficient, adaptive, and robust multi-modal intelligence extraction.

OBJECTS OF THE PRESENT DISCLOSURE
[0006] A general object of the present disclosure relates to a neuromorphic hardware architecture for multi-modal sensor fusion to integrate biomimetic processing elements with quantum-inspired computing principles.
An object of the present disclosure relates to a system and a method for real-time fusion of heterogeneous sensor data, thereby enabling real-time and adaptive fusion of heterogeneous sensor data.
[0007] Another object of the present disclosure is to provide a neuromorphic processing unit for modality-specific sensor data processing, thereby preparing extracted features for integration with other modalities.
[0008] Another object of the present disclosure is to provide a quantum-inspired tensor network processor for sensor fusion applications.
[0009] Another object of the present disclosure is to provide an adaptive attention mechanism for multi-modal sensor fusion, thereby coordinating allocation of computational resources across all the heterogeneous sensor modalities
[0010] Another object of the present disclosure is to provide a cross-modal calibration module for maintaining and dynamically updating relationships among heterogeneous sensor modalities, thereby enabling continuous adaptation of inter-modality correlations without reliance on explicit ground truth data.
[0011] Yet another object of the present disclosure is to provide an energy-harvesting power management subsystem for sensor fusion systems.

SUMMARY
[0012] Aspects of the present disclosure pertain to a neuromorphic hardware architecture designed for multi-modal sensor fusion. In particular, the present disclosure relates to a system and method for real-time fusion of heterogeneous sensor data using adaptive quantum-inspired processing elements for intelligence extraction across diverse sensing modalities. The processing elements may be interconnected through a non-von Neumann routing fabric, which supports dynamic reconfiguration of processing pathways in response to evolving environmental conditions and operational objectives.
[0013] In an aspect, the system may include processors associated with one or more domain-specific Neuromorphic Processing Units (NPUs), each optimized for processing different sensing modalities such as visual, auditory, or radio frequency (RF) data. The system may extract one or more features specific to a corresponding sensing modality from sensor data for integration with other modalities. The system determines high-dimensional probability distributions associated with each of a heterogeneous- sensor modalities by processing the extracted features using a tensor network. Further, the system allocates computational resources to each of the heterogeneous sensor modalities based on the high-dimensional probability distributions and information gain of each of the heterogeneous sensor modalities, and temporally aligns the sensor data using variable-precision timestamps and hardware-accelerated time warping circuits based on the computational resources. Upon aligning the sensor data, the system may generate fused data representative of a state of an environment by evaluating the high-dimensional probability distributions.
[0014] Another embodiment of the present disclosure pertains to a method for real-time fusion of heterogeneous sensor data. The method may include acquiring, by a system, sensor data from the heterogeneous sensor modalities and extracting, one or more features specific to a corresponding sensing modality from the sensor data. The method may include determining, by the system, high-dimensional probability distributions associated with each of the heterogeneous sensor modalities by processing the one or more extracted features using a tensor network and allocating computational resources to each of the heterogeneous sensor modalities based on the high-dimensional probability distributions and information gain of each of the heterogeneous sensor modalities. Further, the method may include temporally aligning, by the system, the sensor data using variable-precision timestamps and hardware-accelerated time warping circuits based on the computational resources. Upon aligning the sensor data, generating, by the system, fused data representative of a state of an environment by evaluating the high-dimensional probability distributions.
[0015] In an embodiment, the system enables, via a probabilistic routing engine, formation of processing pathways to dynamically route the extracted features prior to determining the high-dimensional probability distributions.
[0016] In an embodiment, the probabilistic routing engine may include a plurality of routing nodes interconnected in a three-dimensional mesh, a routing logic to make routing decisions based on global context, and reinforcement learning modules to update routing preferences based on success signals.
[0017] In an embodiment, each of the domain-specific neuromorphic processing units may include a modality-specific front-end interface for direct sensor connectivity, a neuromorphic processor including spiking neural networks with spatiotemporal pattern encoding module, a feature extraction module, a temporal coherence module, and an uncertainty estimation module, and a cross-modal interface for processing the extracted features for integration with other modalities.
[0018] In an embodiment, the tensor network may include tensor processing units arranged in a reconfigurable mesh, a dedicated hardware for tensor contraction, decomposition, and factorization operations, and a sampling circuit for direct sampling of sensor data from high-dimensional probability distributions.
[0019] In an embodiment, the system allocates the computational resources to each of the plurality of heterogeneous sensor modalities, by applying an adaptive attention mechanism through an adaptive attention engine.
[0020] In an embodiment, the adaptive attention engine uses entropy-based estimation circuits to evaluate the information gain of each of the plurality of heterogeneous sensor modalities in real-time.
[0021] In an embodiment, the adaptive attention engine may include attention controllers corresponding to each of the plurality of heterogeneous sensor modalities, and a cross-modal attention arbitrator that coordinates allocation of computational resources across all the heterogeneous sensor modalities.
[0022] In an embodiment, the system may include a cross-modal calibration module to maintain relationships between the plurality of heterogeneous sensor modalities, and continuously adapt inter-modality relationships without requiring explicit ground truth.
[0023] In an embodiment, the cross-modal calibration module may include pairwise calibrators to learn mappings between modality pairs among the plurality of heterogeneous sensor modalities, a consistency enforcer that ensures coherence across all the heterogeneous sensor modalities, and drift compensation circuits that detect and adapt to changing sensor characteristics.
[0024] In an embodiment, the system may be configured to detect anomalies through identification of inconsistencies between the plurality of heterogeneous sensor modalities.
[0025] In an embodiment, the system may include a power management subsystem to combine one or more energy sources and provide stable power to the one or more processors.
[0026] In an embodiment, the system may include a memory subsystem. The memory subsystem may include memristive arrays for immediate sensory context, a phase-change memory for persistent representation learning, and a non-volatile flash memory for long-term data storage.
[0027] In an embodiment, the system may be implemented as a three dimensional (3D) stacked integrated circuit package for real-time fusion of heterogeneous sensor data. The 3D-stacked integrated circuit may include one or more silicon dies, where the one or more silicon dies may be interconnected using Through-Silicon Vias (TSVs) for providing high-bandwidth, low-latency inter-layer connections. Further, the package may include a top layer including a memory subsystem and a controller logic, a middle layer including Neuromorphic Processing Units (NPUs) and a tensor network processor, and a bottom layer including analog front-ends for different sensing modalities and an energy harvesting circuitry.
[0028] Various objects, features, aspects, and advantages of the inventive subject matter will become more apparent from the following detailed description of preferred embodiments, along with the accompanying drawing figures in which like numerals represent components.

BRIEF DESCRIPTION OF THE DRAWINGS
[0029] The accompanying drawings are included to provide a further understanding of the present disclosure and are incorporated in and constitute a part of this specification. The drawings illustrate exemplary embodiments of the present disclosure and, together with the description, serve to explain the principles of the present disclosure.
[0030] FIG. 1 illustrates an example block diagram of a system for real-time fusion of heterogeneous sensor data using adaptive quantum-inspired processing elements, in accordance with embodiments of the present disclosure.
[0031] FIG. 2A illustrates an example implementation of the system for real-time fusion of heterogeneous sensor data using adaptive quantum-inspired processing elements, in accordance with embodiments of the present disclosure.
[0032] FIG. 2B illustrates an example architecture of a neuromorphic processing unit (NPU), in accordance with embodiments of the present disclosure.
[0033] FIG. 2C illustrates an example block diagram of a tensor network, in accordance with embodiments of the present disclosure.
[0034] FIG. 2D illustrates an example block diagram of an adaptive attention engine, in accordance with embodiments of the present disclosure.
[0035] FIG. 2E illustrates an example block diagram of a cross-modal calibration module, in accordance with embodiments of the present disclosure.
[0036] FIG. 2F illustrates a flowchart for operation of a probabilistic routing engine, in accordance with embodiments of the present disclosure.
[0037] FIG. 2G illustrates an example block diagram of a hierarchical memory organization with different memory technologies, in accordance with embodiments of the present disclosure.
[0038] FIG. 2H illustrates an example block diagram of a power management subsystem, in accordance with embodiments of the present disclosure.
[0039] FIG. 2I illustrates an example block diagram of a temporal alignment subsystem, in accordance with embodiments of the present disclosure.
[0040] FIG. 3 illustrates an exemplary flow chart of a method for real-time fusion of heterogeneous sensor data with adaptive quantum-inspired processing elements, in accordance with embodiments of the present disclosure.
[0041] FIG. 4 illustrates an example physical implementation of the system in an integrated circuit package, in accordance with embodiments of the present disclosure.

DETAILED DESCRIPTION
[0042] In the following description, for the purposes of explanation, various specific details are set forth in order to provide a thorough understanding of embodiments of the present disclosure. It will be apparent, however, that embodiments of the present disclosure may be practiced without these specific details. Several features described hereafter can each be used independently of one another or with any combination of other features. An individual feature may not address all of the problems discussed above or might address only some of the problems discussed above. Some of the problems discussed above might not be fully addressed by any of the features described herein.
[0043] The ensuing description provides exemplary embodiments only, and is not intended to limit the scope, applicability, or configuration of the disclosure. Rather, the ensuing description of the exemplary embodiments will provide those skilled in the art with an enabling description for implementing an exemplary embodiment. It should be understood that various changes may be made in the function and arrangement of elements without departing from the scope of the disclosure as set forth.
[0044] Specific details are given in the following description to provide a thorough understanding of the embodiments. However, it will be understood by one of ordinary skill in the art that the embodiments may be practiced without these specific details. For example, circuits, systems, networks, processes, and other components may be shown as components in a block diagram form in order not to obscure the embodiments in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail in order to avoid obscuring the embodiments.
[0045] Also, it is noted that individual embodiments may be described as a process which is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed but could have additional steps not included in a figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination can correspond to a return of the function to the calling function or the main function.
[0046] The word “exemplary” and/or “demonstrative” is used herein to mean serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter disclosed herein is not limited by such examples. In addition, any aspect or design described herein as “exemplary” and/or “demonstrative” is not necessarily to be construed as preferred or advantageous over other aspects or designs, nor is it meant to preclude equivalent exemplary structures and techniques known to those of ordinary skill in the art. Furthermore, to the extent that the terms “includes,” “has,” “contains,” and other similar words are used in either the detailed description or the claims, such terms are intended to be inclusive in a manner similar to the term “comprising” as an open transition word without precluding any additional or other elements.
[0047] Reference throughout this specification to “one embodiment” or “an embodiment” or “an instance” or “one instance” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, the appearances of the phrases “in one embodiment” or “in an embodiment” in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
[0048] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. As used herein, the term “and/or” includes any and all combinations of one or more of the associated listed items.
[0049] The present disclosure relates generally to multi-modal data fusion systems and methods, and more particularly to a system and method for real-time fusion of heterogeneous sensor data using adaptive quantum-inspired processing elements for intelligence extraction across diverse sensing modalities.
[0050] The present disclosure relates to a neuromorphic system specifically designed for real-time multi-modal sensor fusion. The system integrates biomimetic processing elements, quantum-inspired tensor networks, and adaptive hardware control mechanisms to enable efficient and intelligent data processing. The system may include neuromorphic processing units, each optimized for a particular sensing modality, and interconnected via a dynamically reconfigurable non-von Neumann routing fabric. The system may include quantum-inspired processing elements configured to implement tensor network computations in dedicated hardware and hardware-embedded attention engine to allocate computational resources based on relevant sensor inputs. Further, the system may include a hierarchical memory organization, utilizing a combination of memristive components for short-term context and phase-change memory for long-term knowledge retention. The system may include a cross-modal calibration module to continuously adapt inter-sensor relationships over time. The system enables energy-efficient, resilient, and adaptive extraction of high-level intelligence from heterogeneous and asynchronous sensor data streams.
[0051] In an aspect, the system may include neuromorphic processors, each optimized for processing different sensing modalities such as visual, auditory, or radio frequency (RF) data. The system may extract one or more features specific to a corresponding sensing modality from the sensor data for integration with other modalities. The system determines high-dimensional probability distributions associated with each of the heterogeneous sensor modalities by processing the extracted features using a tensor network processor. Further, the system allocates computational resources to each of the heterogeneous sensor modalities based on the high-dimensional probability distributions and information gain of each of the heterogeneous sensor modalities, and temporally aligns the sensor data using variable-precision timestamps and hardware-accelerated time warping circuits based on the computational resources. Upon aligning the sensor data, the system may generate fused data representative of a state of an environment by evaluating the high-dimensional probability distributions.
[0052] Another embodiment of the present disclosure pertains to a method for real-time fusion of heterogeneous sensor data. The method may include acquiring, by a system, sensor data from heterogeneous sensor modalities and extracting one or more features specific to a corresponding sensing modality from the sensor data. The method may include determining, by the system, high-dimensional probability distributions associated with each of the of heterogeneous sensor modalities by processing the one or more extracted features using a tensor network processor and allocating computational resources to each of the heterogeneous sensor modalities based on the high-dimensional probability distributions and information gain of each of heterogeneous sensor modalities. Further, the method may include temporally aligning, by the system, the sensor data using variable-precision timestamps and hardware-accelerated time warping circuits based on the computational resources. Upon aligning the sensor data, generating, by the system, fused data representative of a state of an environment by evaluating the high-dimensional probability distributions.
[0053] Various embodiments of the present disclosure will be explained in detail with reference to FIGs. 1-4.
[0054] FIG. 1 illustrates an example block diagram (100) of a system (102) for real-time fusion of heterogeneous sensor data with adaptive quantum-inspired processing elements, in accordance with embodiments of the present disclosure.
[0055] With reference to FIGs. 1, in an embodiment, the system (102) may include processors (104). In an embodiment, the system (102) may be associated with different sensing modalities such as visual, auditory, or radio frequency (RF) data. The processors (104) may be implemented as one or more microprocessors, neuromorphic processors, microcomputers, microcontrollers, digital signal processors, central processing units, logic circuitries, and/or any devices that manipulate data based on operational instructions. Among other capabilities, the processors (104) may be configured to fetch and execute computer-readable instructions stored in a memory (106). The memory (106) may store one or more computer-readable instructions or routines, which may be fetched and executed to create or share the data units over a network service. The memory (106) (may be implement as a memory subsystem) may include any non-transitory storage device, including, for example, volatile memory such as a Random Access Memory (RAM), or a non-volatile memory such as an Erasable Programmable Read-Only Memory (EPROM), a flash memory, and the like.
[0056] In some embodiments, the system (102) may also include an interface(s) (108). The interface(s) (108) may include a variety of interfaces, for example, interfaces for data input and output devices, referred to as I/O devices, storage devices, and the like. The interface(s) (108) may facilitate communication between the processors (104) and other components of the system, using peripherals that allow wired and/or wireless communication. The interface(s) (108) may also provide a communication pathway for one or more components within the system (102). Examples of such components include, but are not limited to, processing engine(s) and the memory.
[0057] In an embodiment, the processing engine(s) (110) may be implemented as a combination of hardware and programming (for example, programmable instructions) to implement one or more functionalities of the processing engine(s) (110). In examples described herein, such combinations of hardware and programming may be implemented in several different ways. For example, the programming for the processing engine(s) (110) may be processors (104) executable instructions stored on a non-transitory machine-readable storage medium and the hardware for one or more processor(s) (104) may comprise a processing resource (for example, one or more processors), to execute such instructions. In the present examples, the machine-readable storage medium may store instructions that, when executed by the processing resource, implement the processing engine(s) (110). In such examples, the system (102) may comprise the machine-readable storage medium storing the instructions and the processing resource to execute the instructions, or the machine-readable storage medium may be separate but accessible to the system (102) and the processing resource. In other examples, the processing engine(s) (110) may be implemented by an electronic circuitry.
[0058] Further, the processing engine(s) (110) may include an acquiring module (112), an extracting module (114), a determining module (116), an allocating module (118), an aligning module (120), a generating module (122) and other module(s) (124). The other module(s) (124) may implement functionalities that supplement applications/functions performed by the processing engine(s) (110).
[0059] In an embodiment, the system (102) may include processors (104) and a memory storing instructions that, when executed, enable the acquisition of sensor data from a plurality of heterogeneous sensor modalities via domain-specific neuromorphic processors in a Neuromorphic Processing Unit (NPU) (126). Modality-specific features may be extracted from the sensor data and processed using a tensor network (128) to generate high-dimensional probability distributions. The system (102) may allocate computational resources to each of the plurality of heterogeneous sensor modalities based on an evaluation of the corresponding high-dimensional probability distributions and the information gain associated with each modality. Temporal alignment across asynchronous sensor streams may be achieved through variable-precision timestamps and hardware-accelerated time warping circuits. Upon aligning the sensor data, the system (102) may generate a fused data representative of an environmental state by evaluating the previously determined high-dimensional probability distributions. The fused data reflects integrated contextual information derived from multiple sensor modalities, enabling energy-efficient, adaptive, and robust multi-modal intelligence extraction for use in real-time applications.
[0060] In an embodiment, the system (102) may include domain-specific neuromorphic processors in the Neuromorphic Processing Unit (NPU) (126). The acquiring module (112) may be configured to acquire sensor data from heterogeneous sensor modalities using a neuromorphic processor (220). Each Neuromorphic Processing Unit (NPU) (126) includes a modality-specific front-end (218) to interface sensor associated with the NPU (126). The system (102) may include domain-specific neuromorphic processors optimized for different sensing modalities (visual, auditory, RF, etc.). The acquiring module (112) may acquire sensor data through the modality-specific front-end (218) of each neuromorphic processor (220).
[0061] In an embodiment, the extracting module (114) may be configured to extract one or more features specific to a corresponding sensing modality from the sensor data. The domain-specific neuromorphic processors, each architected to support unique characteristics of a particular sensor modality, such as visual neuromorphic processor (may be included in NPU (126a)) may employ a foveated processing structure using a mixed-signal implementation to efficiently extract visual features while concentrating computational effort on regions of interest, auditory neuromorphic processor (may be included in NPU (126b)) incorporates a cochlear-inspired frequency decomposition mechanism using coupled oscillators to enable low-power, high-fidelity spectro-temporal feature recognition and RF neuromorphic processor (may be included in NPU (126c)) may use a hybrid approach combining compressed sensing with neuromorphic acceleration to detect and classify signals in complex spectral environments. The extracting module (114) may extract features by using analysis functions based on unique characteristics of a particular sensor modality.
[0062] In an embodiment, the determining module (116) may be configured to determine high-dimensional probability distributions associated with each of the heterogeneous sensor modalities by processing the extracted features using a tensor network (128). The system (102) may include the tensor network processor. The tensor network (128) may include multiple tensor processing units (TPUs) (234a to 234j) arranged in a reconfigurable mesh (232). Each TPU (234a to 234j) may implement operations for tensor contraction (236), decomposition (238), and factorization (240) in dedicated hardware. A tensor network compiler (244) automatically maps fusion algorithms to this hardware architecture, optimizing for both computational efficiency and memory access patterns. The determining module (116) may calculate joint probability distributions across different sensing modalities, enabling coherent Bayesian fusion of heterogeneous data sources. The system (102) may include specialized hardware for tensor train decomposition, allowing efficient representation of high-dimensional probability tensors with significantly reduced memory requirements compared to explicit representations.
[0063] In an embodiment, the allocating module (118) may be configured to allocate computational resources to each of the plurality of heterogeneous sensor modalities based on the high-dimensional probability distributions and information gain of each of the plurality of heterogeneous sensor modalities. The system (102) may include an adaptive attention engine (130) implemented in hardware to operate across heterogeneous sensing modalities. The adaptive attention engine (130) dynamically allocates computational resources based on the current information content and relevance of different sensor streams. The system (102) includes a set of modality-specific attention controllers, each generating attention masks to modulate processing for its respective input stream. The system (102) may include a cross-modal attention arbitrator (246) to coordinate controllers (248a, 248b, 248c, .. 248n) to manage shared resources and maintain system-wide coherence in fusion tasks. The allocating module (118) employs an information-theoretic approach to attention, using hardware-accelerated entropy-based estimation circuits (250) to assess the expected information gain from each modality.
[0064] In an embodiment, the aligning module (120) may temporally align the sensor data using variable-precision timestamps and hardware-accelerated time warping circuits based on the computational resource. The adaptive attention engine (130) operates at multiple timescales, from millisecond-level reactive attention to longer-term focus on persistently relevant sources. The system (102) may include a cascade of feedback paths with different time constants, implemented directly in analog circuitry for energy efficiency.
[0065] In an embodiment, upon aligning the sensor data, the generating module (122) may be configured to generate fused data representative of a state of an environment by evaluating the high-dimensional probability distributions. The generation module evaluates high-dimensional probability distributions to integrate and synthesize information from the sensor modalities, thereby ensuring that the system can handle complex, heterogeneous sensor streams and adapt to varying environmental contexts.
[0066] In an embodiment, the system (102) may include a cross-modal calibration module (132) to maintain relationships between the heterogeneous sensor modalities, and continuously adapt inter-modality relationships without requiring explicit ground truth. The cross-modal calibration module (132) implements continual learning of relationships through dedicated hardware by maintaining statistical models of the relationships between different sensing modalities. The cross-modal calibration module (132) may include multiple pairwise calibration units, such as visual-attention calibration (254a), visual-RF calibration (254b), auditory-RF calibration (254c), to learn mappings between specific modality pairs, a global consistency enforcer to ensure coherence across all modalities, and drift compensation circuits (256) to detect and adapt to changing sensor characteristics. Further, the cross-modal calibration module (132) may include a self-supervised learning engine (256) where consistent patterns across modalities serve as natural supervisory signals, enabling continuous adaptation without requiring explicit ground truth. The cross-modal calibration module (132) may include hardware implementation of contrastive learning principles, where the system (102) maximizes mutual information between representations of the same event across different modalities.
[0067] In an embodiment, the system (102) enables, via a probabilistic routing engine (134), formation of processing pathways to dynamically route the extracted features prior to determining the high-dimensional probability distributions. The probabilistic routing engine (134) may provide deterministic routing and enables dynamic formation of processing pathways optimized for specific fusion tasks. The probabilistic routing engine (134) may include a process analogous to synaptic potentiation for strengthening successful processing pathways, thereby enabling the system to discover optimal processing configurations through experience rather than explicit programming.
[0068] In an embodiment, the probabilistic routing engine (134) may include routing nodes (258) interconnected in a three-dimensional mesh, a routing logic (260) to make routing decisions based on global context, and reinforcement learning module (262) to update routing preferences based on success signals.
[0069] In an embodiment, the system (102) may be configured to detect anomalies through identification of inconsistencies between the plurality of heterogeneous sensor modalities.
[0070] An example implementation of the system (102) for real-time fusion of heterogeneous sensor data with adaptive quantum-inspired processing elements is shown in FIGs. 2A to 2I. Referring to FIG. 2A, FIG. 2A illustrates the system-level block diagram (200A) of the neuromorphic multi-modal fusion architecture. The system (102) may include multiple domain-specific neuromorphic processing unit (NPUs) (126a, 126b, 126c,...., 126n), each optimized for a particular sensing modality. The NPUs (126a, 126b, 126c,...., 126n) may connect to other components of the system (102) through a probabilistic routing engine (134) to enable dynamic reconfiguration of data pathways. A tensor network (128) may provide efficient handling of high-dimensional probability distributions. The system (102) may include a memory subsystem (214), an adaptive attention engine (130), and a cross-modal calibration module (132). Further, the system (102) may include a contextual controller (140) to manage global optimization and resource allocation and a power management subsystem (138) to ensure efficient operation in resource-constrained environments.
[0071] Referring to FIG. 2B, FIG. 2B illustrates the internal structure of a domain-specific Neuromorphic Processing Unit (NPU) (126a, 126b, 126c,...., 126n). Each NPU (126a, 126b, 126c,...., 126n) may specifically optimized for statistical properties and processing requirements of associated modality, such as visual NPU (126a) may include a foveated processing architecture with variable resolution that concentrates computational resources on regions of interest, mimicking the human visual system. The system (102) may include a mixed-signal circuit (230) to combine analog preprocessing for energy efficiency with digital precision. Further, the NPU (126) may include specialized hardware modules designed to support advanced data processing tasks, including a feature extraction module (224) for identifying and encoding salient patterns from raw sensor inputs, a temporal coherence module (226) for preserving consistency across time-series data, and an uncertainty estimation module (228) for quantifying the reliability and confidence levels of the fused sensor outputs. The domain-specific neuromorphic processing unit (NPU) (126a) tailored to specific sensing modalities may be a visual NPU may include a foveated architecture to mimic human vision by concentrating computational resources on regions of interest and combines energy-efficient analog preprocessing with precise digital computation by using a mixed-signal design. The domain-specific neuromorphic processing unit (NPU) (126b) may be an auditory NPU, may use a cochlear-inspired frequency decomposition implemented through coupled oscillators to enable low-power, real-time spectro-temporal pattern recognition, with sparse coding circuitry to extract meaningful features and suppress noise. Further, the domain-specific neuromorphic processing unit (NPU) (126c) may be an RF NPU to integrate compressed sensing with neuromorphic processing and may include hardware accelerators for spectrum analysis, modulation classification, and direction finding, enabling robust detection of signals in crowded RF environments.
[0072] Each NPU (126a, 126b, 126c,...., 126n) may include a modality-specific front-end (218) to interface directly with the associated sensor, a neuromorphic processor (220) to implement primary analysis functions, and a cross-modal interface (222) to prepare extracted features for integration with other modalities. The cross-modal interface (222) may map different sensor or data types (modalities) into a shared embedding space with a same dimensionality, such as 256 features for all modalities, and may ensure that the output representations are both expressive and comparable across very different input types. The NPUs (126a, 126b, 126c,...., 126n) may include spiking neural networks where information may be encoded in precise spatiotemporal patterns. The system (102) enables efficient information encoding by combining energy efficiency of analog computation with a precision of digital control through a custom mixed-signal circuit implementation.
[0073] Referring to FIG. 2C, FIG. 2C illustrates the tensor network (128). The tensor network (128) may include multiple Tensor Processing Units (TPUs) (234a to234j) arranged in a reconfigurable mesh (232). Each TPU (234a to 234j) may include operations for tensor contraction (236), tensor decomposition (238), and tensor factorization (240) in dedicated hardware. A tensor network compiler (244) automatically maps fusion algorithms to the tensor network (128), thereby optimizing for both computational efficiency and memory access patterns. The quantum-inspired processor may include a probability distribution calculator (354) for calculating joint probability distributions across different sensing modalities, enabling coherent Bayesian fusion of heterogeneous data sources. The tensor network (128) may include specialized hardware for tensor train decomposition (350), thereby allowing efficient representation of high-dimensional probability tensors with significantly reduced memory requirements compared to explicit representations. The tensor network (128) may include quantum-inspired sampling circuits (242) to allow direct sampling from complex probability distributions without requiring explicit computation of the full distribution thereby enabling efficient inference even in high-dimensional spaces that would be intractable with conventional approaches. The tensor network (128) may also include an inference engine (352) and a high-dimensional sampling module (356), where the inference engine (352) enables fast, energy-efficient optimization of high-dimensional models. The high dimensional sampling module (356) implements parallel gradient computation, high-precision leapfrog integration, and adaptive step size control to maintain high acceptance rates. By leveraging dedicated circuits, such as for random momentum generation, kinetic energy calculation, the high dimensional sampling module (356) achieves fast, accurate sampling with low power consumption.
[0074] Referring to FIG. 2D, FIG. 2D illustrates the adaptive attention engine (130) associated with the system (102) to dynamically allocate computational resources based on current information content and relevance of different sensor streams. The adaptive attention engine (130) may include multiple attention controllers (248a, 248b, 248c,...248n), each corresponding to a specific sensing modality. In an example, a visual attention controller (248a) for visual sensors, an auditory attention controller (248b) for audio sensors, a radio frequency (RF) attention controller (248c) for RF sensors may be included in the adaptive attention engine (130). The attention controllers (248a, 248b, 248c,...248n) may generate attention masks, such as a mask for video inputs, a mask for audio input, a mask for radio frequency, to modulate processing of associated sensor inputs. The adaptive attention engine (130) may include a cross-modal attention arbitrator (246) to coordinate modalities to ensure coherent allocation of resources. The system (102) may include information-theoretic attention to allocate computational resources in proportion to expected information gain from each sensor, by using hardware-accelerated entropy-based estimation circuits (250). The adaptive attention engine (130) allows efficient utilization of limited processing capabilities by focusing on the most informative data sources in any given situation. Further, the adaptive attention engine (130) may operate at multiple timescales, from millisecond-level reactive attention to longer-term focus on persistently relevant sources. The multi-scale operation may be implemented through a cascade of feedback paths with different time constants and may be implemented directly in analog circuitry for energy efficiency. The adaptive attention engine (130) may include an information gain calculator (250b) to calculate information gain for predictive tasks. The adaptive attention engine (130) may be associated with a multi scale decomposition 250a and resource allocation process.
[0075] Referring to FIG. 2E, FIG. 2E illustrates the cross-modal calibration module (132). The cross-modal calibration module (132) may be configured to establish and maintain appropriate relationships between measurements from different sensing modalities, especially as sensor characteristics drift over time. The cross-modal calibration module (132) may include continual learning of relationships between measurements from different sensing modalities through dedicated hardware to maintain statistical models of the relationships between different sensing modalities. The cross-modal calibration module (132) may include multiple pairwise calibration units, such as visual-attention calibration (254a), visual-RF calibration (254b), auditory-RF calibration (254c), to learn mappings between specific modality pairs, a global consistency enforcer (252) to ensure coherence across all modalities, and a drift compensation circuits (256) to detect and adapt to changing sensor characteristics. The cross-modal calibration module (132) may include a self-supervised learning engine (256) where consistent patterns across modalities serve as natural supervisory signals, enabling continuous adaptation without requiring explicit ground truth. The cross-modal calibration module (132) may include hardware implementation of contrastive learning principles, where the system (102) maximizes mutual information between representations of the same event across different modalities. The self-supervised learning engine (256) may be associated with a drift compensation circuit (256-1) and a calibration quality monitor (256-2). The drift compensation circuit (256-1) may combine a discrete Kalman filter hardware core with environmental compensation lookup tables and real-time calibration quality monitoring features. The calibration quality monitor (256-2) may provide real-time, measurable assessment of sensor calibration through a dedicated quality metrics processor that tracks accuracy, stability, and confidence.
[0076] Referring to FIG. 2F, FIG. 2F illustrates a flowchart (200F) for operation of the probabilistic routing engine (134) associated with the system (102). The probabilistic routing engine (134) may implement reinforcement learning directly in hardware, where successful processing pathways may be strengthened through a process analogous to synaptic potentiation. The probabilistic routing engine (134) may include multiple routing nodes (258) interconnected in a three-dimensional mesh. Each node may include routing logic (260) to make local decisions based on global context signals and local congestion information. A reinforcement learning module (262) within each node updates routing preferences based on success signals propagated backward from fusion outputs, thereby discovering optimal processing configurations through experience rather than explicit programming. The probabilistic routing engine (134) enables the formation of virtual processing pipelines to dynamically adapt to changing operational requirements and sensor availability, without explicit reconfiguration, thereby significantly enhancing system robustness in complex and unpredictable environments.
[0077] Referring to FIG. 2G, FIG. 2G illustrates the memory subsystem (214) associated with the system (102). The memory subsystem (214) combines multiple memory technologies with different characteristics to create a hierarchy optimized for sensor fusion tasks. Lowest level memory may include ultra-fast volatile analog memristive arrays (276) to store immediate sensory context and short-term correlations between modalities. The memristive arrays (276) may implement matrix operations required for neural processing with minimal energy consumption, enabling real-time adaptation to changing conditions. Middle level memory may include phase-change memory (PCM) arrays (278) to store more persistent representations learned over longer time periods. The PCM elements may implement synaptic plasticity mechanisms directly in hardware, enabling continuous learning from sensory experiences without explicit training phases. Highest level memory may include high-density non-volatile flash memory, such as NAND flash memory (280) to store long-term data, including pre-trained models and accumulated experience. The memory subsystem (214) may include a memory controller (274) to coordinate information flow between different level memories. The memory subsystem may include a sparse addressing module (272) to allocate memory resources based on information content rather than fixed addressing, significantly improving efficiency for the highly skewed probability distributions typical in sensory data.
[0078] Further, the memory subsystem (214) may include an information content based allocation module (358) and a neuromorphic consolidation mechanism (360), where the consolidation mechanism may be controlled via a consolidation controller (358a). The neuromorphic consolidation mechanism (360) emulates brain-like learning by integrating a hierarchical memory system that combines fast, activity-dependent encoding using memristor crossbars, robust intermediate storage with phase-change memory (PCM), and long-term retention in high-density NAND flash memory (280). The neuromorphic consolidation mechanism (360) enables rapid adaptation to new inputs, stabilization of frequently accessed information, and durable storage of critical knowledge such as pre-trained models and accumulated experience. The neuromorphic consolidation mechanism (360) may also use rapid encoding through spike-timing dependent plasticity (STDP) circuits, enabling real-time synaptic weight updates, pattern detection and stability assessment modules to ensure only statistically significant, persistent patterns are consolidated, and a transfer control mechanism for transferring data between memory layers based on stability scores, using compression and error correction for efficiency and reliability.
[0079] Referring to FIG. 2H, FIG. 2H illustrates the power management subsystem (138) associated with the system (102). The system (102) may include an integrated power management system to combine multiple energy harvesting modalities with intelligent power distribution. The power management subsystem (138) may include a photovoltaic harvesting circuit (282), a thermal gradient harvester (284), a vibration energy capture module (286), and an RF energy harvester (288). Each of the multiple energy harvesting modalities may include Maximum Power Point Tracking (MPPT) modules to maximize energy extraction in various conditions. The power management subsystem (138) may include a unified power conditioning circuit (290) to combine multiple energy sources to provide stable power to the processors. Further, the system (102) may include an intelligent power manager (292) for implementing dynamic voltage and frequency scaling across different processing modules based on current workloads and available energy. The intelligent power manager (292) may include a predictive energy model (362) that uses a machine learning approach, specifically a three-layer neural network, to forecast solar energy availability based on time, date, weather forecasts, and historical irradiance data.
[0080] The intelligent power manager (292) may also include a dynamic voltage frequency scaling module (364), an importance aware power gating module (366), and an energy storage module (364). The dynamic voltage frequency scaling module (364) uses real-time hardware performance counters and a power-performance model to optimize power for minimal power per unit performance, thereby ensuring efficient, adaptive scaling across workloads and temperature conditions. The importance aware power gating module (366) uses a hierarchical, priority-based system to maximize energy savings while ensuring critical system functions remain uninterrupted. The importance aware power gating module (366) may assign each functional block to one of four priority levels, such as critical, high, medium, or low, with gating granularity at per-block level. Critical functions (such as safety monitoring, emergency communications, core operating system (OS), and battery management) are always powered, while lower-priority blocks can be power-gated with state retention via non-volatile memory, thereby enabling robust, context-aware power management for safety-critical and energy-constrained embedded systems. The energy storage module (364) may combine a high-capacity supercapacitor bank with a robust battery backup to deliver both rapid power bursts and long-term energy supply. The energy storage module (364) features active cell balancing via fly back converters, precise state-of-charge tracking (coulomb counting and voltage), regular Equivalent Series Resistance (ESR) and temperature monitoring, and comprehensive safety protections.
[0081] Further, the power management subsystem (138) may include a power distribution module (368) to distribute power to each component in the system (102). The power management subsystem (138) may include predictive models of energy availability to enable proactive adjustment of processing capabilities to match expected resources. The power subsystem (138) may implement importance-aware power gating to preferentially maintain power to the most critical functions during energy constraints, thereby enabling graceful degradation rather than complete failure when resources are limited.
[0082] Referring to FIG. 2I, FIG. 2I illustrates a temporal alignment subsystem (370) associated with the system (102). The temporal alignment subsystem (370) may synchronize heterogeneous sensor data streams with different sampling rates, processing latencies, and temporal characteristics. The temporal alignment subsystem (370) may include a variable-precision timestamping module (372) that uses adaptive precision allocation based on temporal requirements of each sensing modality, such as a visual sensor (380a), an audio sensor (380b), a RF sensor (380c), and inertial measurement units (IMU) sensor (380d). High-frequency sensors such as RF sensor (380c) receive microsecond-precision timestamps, while lower-frequency environmental sensors use millisecond precision, optimizing memory usage while maintaining required temporal accuracy for each application. The temporal alignment subsystem (370) may include a hardware time warping circuit (374), synchronization controllers (376), and temporal buffers (378). The hardware time warping circuit (374) use dynamic time warping algorithms in dedicated hardware for real-time temporal alignment of asynchronous data streams and handles elastic time relationships between modalities, accommodating natural variations in sensor response times and processing delays that occur in real-world deployments. The synchronization controllers (376) maintain phase relationships between different sensor clocks and implement predictive algorithms to compensate for missing or delayed measurements using learned temporal patterns specific to each sensor modality. The temporal alignment subsystem (370) operates continuously and maintains a unified temporal reference frame across all sensing modalities while adapting to changing operational conditions and sensor characteristics. Further, the temporal buffer (378) may use hardware-based circular addressing, watermark-triggered processing, oldest-data replacement on overflow, and timestamp synchronization to ensure efficient, real-time sequence comparison and alignment. The temporal alignment subsystem (370) may also include a temporal alignment processing pipeline (382), and a temporally aligned sensor streams (384) associated with the temporal buffer (378).
[0083] FIG. 3 illustrates an exemplary flow chart of a method (300) for real-time fusion of heterogeneous sensor data with adaptive quantum-inspired processing elements, in accordance with embodiments of the present disclosure.
[0084] Referring to FIG. 3, the method (300) may include one or more steps performed by the processors (304) associated with the system (102). At step (302), the method may (300) include acquiring sensor data from heterogeneous sensor modalities. The sensor modalities may be such as visual, auditory, or radio frequency (RF) data. At step (304), the method (300) may include extracting one or more features specific to a corresponding sensing modality from the sensor data. Modality specific features may be extracted by using analysis functions based on unique characteristics of a particular sensor modality. At step (306), the method may include determining high-dimensional probability distributions associated with each of the heterogeneous sensor modalities by processing one or more extracted features using the tensor network (128). The system may (102) determine high-dimensional probability distributions by calculating joint probability distributions across different sensing modalities, enabling coherent Bayesian fusion of heterogeneous data sources. At step (308), the method (300) may include allocating computational resources to each of the plurality of heterogeneous sensor modalities. The system (102) may dynamically allocate computational resources based on the current information content and relevance of different sensor streams using the adaptive attention engine. At step (310), the method (300) may include temporally aligning the sensor data using variable-precision timestamps and hardware-accelerated time warping circuits based on the computational resources. At step (312), the method may include generating fused data representative of a state of an environment by evaluating the high-dimensional probability distributions.
[0085] FIG. 4 illustrates an example physical implementation (400) of the system (102) in an integrated circuit package (402), in accordance with embodiments of the present disclosure.
[0086] In an embodiment, referring to FIG. 4, the system (102) may be implemented as or in a heterogeneous three dimensional (3D) stacked integrated circuit package (402) with multiple silicon dies interconnected through-silicon vias (TSVs) and microbumps. Through-Silicon Via (TSV) formation uses deep reactive ion etching with via diameters that are optimized for signal integrity and thermal management. The TSV pattern may provide redundant pathways for critical signals while minimizing parasitic effects.A bottom layer (408) of the 3D stacked integrated circuit package (402) may include the modality specific front ends (218) for different sensing modalities and the power management subsystem (138). The bottom layer (408) may be optimized for analog signal processing and power management, with specialized process technologies for optimal performance in these domains. A middle layer (406) of the 3D stacked integrated circuit package (402) may include the neuromorphic processing units (126a, 126b, 126c,...., 126n) and the tensor network processor (234a to 234j). The middle layer (406) may use advanced digital process technologies optimized for high-performance computing while maintaining energy efficiency. The middle layer (406) may be implemented using high-density memory technologies and may incorporate the control circuitry for system coordination. A top layer (404) of the 3D stacked integrated circuit package (402) may include the memory subsystem (214) and a control logic (410). The top layer (404) integrates multiple memory technologies using process flows that accommodate the different thermal and electrical requirements of memristive, PCM, and flash memory elements.
[0087] The heterogeneous 3D-stacked integrated circuit package (402) enables high-bandwidth, low-latency connections between processing elements while minimizing energy consumption for data movement and also allows optimal placement of different circuit types, with analog circuits isolated from digital switching noise through careful floor planning and power distribution. The heterogeneous 3D-stacked integrated circuit may be designed for modularity, allowing customization for specific application requirements by populating different combinations of sensing front-ends while maintaining a consistent processing architecture.
[0088] In an embodiment, the present disclosure may include several operational modes to enable effective multi-modal intelligence extraction across diverse scenarios.
(1) Adaptive Sensor Recruitment Mode: In the adaptive Sensor Recruitment mode, the system (102) dynamically activates and deactivates sensors based on their current utility for the fusion task at hand. The adaptive Sensor Recruitment mode may be implemented through a reinforcement learning mechanism to evaluate information gain provided by each sensor relative to its energy consumption, automatically discovering optimal sensor combinations for different scenarios. In the adaptive Sensor Recruitment mode operates as follows: (a) Initialize with a minimal sensor set active, (b) Periodically activate additional sensors to sample their current utility, (c) Calculate information gain per energy expenditure for each sensor, (d) Maintain activation of sensors with high utility while deactivating those with low utility, and (e) Periodically re-evaluate to adapt to changing conditions. The adaptive Sensor Recruitment mode operation may enable efficient operation in energy-constrained environments by focusing resources on the most valuable sensing modalities for current situation, extending operational lifetime while maintaining fusion accuracy.
(2) Cross-Modal Translation Mode: In the Cross-Modal Translation Mode, the system (102) may implement a method for cross-modal translation, where information available in one sensing modality can be used to predict or reconstruct information in another modality that may be unavailable or corrupted. The Cross-Modal Translation Mode may be implemented through the quantum-inspired tensor network processor, which maintains probabilistic models of the relationships between different modalities. The cross-modal translation mode process operates as follows: (a) Learn joint probability distributions across modalities during normal operation, (b) When a modality becomes unavailable, use conditional probability calculations to estimate likely values based on available modalities, (c) Incorporate uncertainty estimates in these predictions to avoid overconfidence, and (d) Update translation models continuously based on observed correlations. The Cross-Modal Translation Mode operation may significantly enhance the system robustness in challenging environments where individual sensors may fail or become unreliable, enabling continued operation with graceful degradation rather than complete failure.
(3) Anomaly Detection Through Cross-Modal Inconsistency Mode: In the Anomaly Detection Through Cross-Modal Inconsistency Mode, the system (102) may implement a method for detecting anomalies and potential deception by identifying inconsistencies between different sensing modalities. The Anomaly Detection Through Cross-Modal Inconsistency Mode may particularly valuable in adversarial environments where individual sensors might be spoofed or jammed. The Anomaly Detection Through Cross-Modal Inconsistency Mode operates as follows: (a) Maintain models of normal correlations between sensing modalities, (b) Continuously calculate consistency scores across all active modality pairs, (c) Flagging anomalies when observed correlations deviate significantly from expected patterns, and (d) Adapting detection thresholds based on environmental conditions to balance sensitivity with false alarm rates. The Anomaly Detection Through Cross-Modal Inconsistency Mode operation enables robust operation even in the presence of deliberate interference with individual sensing modalities, providing security against spoofing attacks and environmental interference.

(4) Continual Learning Without Catastrophic Forgetting Mode: The memory subsystem (214) in the system (102) enables continual learning to avoid catastrophic forgetting problem common in neural network systems. By implementing complementary learning systems directly in hardware, the present disclosure can continuously adapt to new conditions while preserving important previously learned knowledge. The Continual Learning Without Catastrophic Forgetting Mode operates as follows: (a) Rapidly encode new experiences in the fast memristive memory layer, (b) Gradually consolidate consistent patterns to the phase-change memory layer, (c) Periodically transfer stable, general knowledge (data) to the non-volatile flash layer, and (d) Use knowledge (data) from all three layers during inference, with appropriate weighting. The Continual Learning Without Catastrophic Forgetting Mode enables the system to adapt to new environments while maintaining performance on previously encountered scenarios, a critical capability for long-term deployment in dynamic conditions.
[0089] In an exemplary implementation, in a comprehensive autonomous vehicle deployment, the system (102) integrates 12 distinct sensing modalities including stereo cameras, thermal imaging, Light Detection and Ranging (LiDAR) arrays, millimeter-wave radar, ultrasonic sensors, Global Positioning System (GPS)/Global Navigation Satellite System (GNSS), inertial measurement units, wheel encoders, steering angle sensors, and vehicle Controller Area Network (CAN) bus data. A visual NPU processes stereo camera data using foveated attention mechanisms that concentrate processing on regions with high optical flow or unexpected objects. A thermal NPU processes infrared data for pedestrian detection in low-visibility conditions. An RF NPU (204c) manages both radar returns and Vehicle-to-Everything (V2X) communication signals. During foggy conditions, the adaptive attention engine (208) automatically reduces weighting on visual sensors while increasing emphasis on radar and LiDAR data. The cross-modal calibration module (132) maintains consistent object tracking as sensor reliability shifts, ensuring smooth handoffs between modalities. The tensor network (128) calculates joint probability distributions for object classification, trajectory prediction, and collision risk assessment across all active sensors simultaneously, enabling more accurate situational awareness than sequential processing approaches.
[0090] In another exemplary implementation, in healthcare applications, the system (102) integrates physiological signals (Electrocardiogram (ECG), Electroencephalogram (EEG), respiration), biochemical measurements, activity patterns, and environmental factors to provide a comprehensive health assessment. The quantum-inspired processor enables accurate modeling of complex interactions between these factors, while the energy management subsystem (138) allows long-term wearable deployment without frequent charging. The anomaly detection through cross-modal inconsistency enables a reliable early warning of developing health conditions.
[0091] In another exemplary implementation, in environmental monitoring applications, the system (102) coordinates across distributed sensor nodes with heterogeneous capabilities. Each node implements full architecture at appropriate scale, with the probabilistic routing engine (134) enabling dynamic formation of processing networks optimized for current monitoring objectives. The cross-modal translation capability allows reconstruction of comprehensive environmental models even when individual sensor types are only sparsely deployed.
[0092] While the foregoing describes various embodiments of the present disclosure, other and further embodiments of the present disclosure may be devised without departing from the basic scope thereof. The scope of the present disclosure is determined by the claims that follow. The present disclosure is not limited to the described embodiments, versions or examples, which are included to enable a person having ordinary skill in the art to make and use the present disclosure when combined with information and knowledge available to the person having ordinary skill in the art.

ADVANTAGES OF THE PRESENT DISCLOSURE
[0093] The present disclosure provides a system a method for real-time fusion of heterogeneous sensor data, thereby enabling real-time and adaptive fusion of heterogeneous sensor data.
[0094] The present disclosure enables efficient operation in energy-constrained environments by focusing resources on the most valuable sensing modalities for the current situation.
[0095] The present disclosure provides a method for cross-modal translation, thereby allowing information available in one sensing modality to be used to predict or reconstruct information in another modality that may be unavailable or corrupted.
[0096] The present disclosure provides a novel neuromorphic hardware architecture for multi-modal sensor fusion that combines biomimetic processing elements with quantum-inspired computing principles to enable robust, energy-efficient, and adaptive intelligence extraction from heterogeneous sensor streams.
[0097] The present disclosure enables dynamic allocation across sensor modalities through an attention engine, ensuring that critical information receives prioritized processing, especially under resource constraints.
[0098] The present disclosure maintains and updates inter-modality relationships over time without requiring explicit ground truth through a dedicated calibration module, thereby enabling resilient and self-correcting multi-sensor alignment.
[0099] The present disclosure provides probabilistic routing, uncertainty estimation, and neuromorphic encoding that collectively improve system resilience against sensor noise, failure, and adversarial conditions.
, Claims:1. A system (102) for real-time fusion of heterogeneous sensor data, the system (102) comprising:
one or more processors (104); and
a memory (106) operatively coupled with the one or more processors (104), wherein the memory comprises one or more instructions which, when executed, cause the system (102) to:
acquire, using one or more domain-specific neuromorphic processing units (126) associated with the one or more processors (104), sensor data from a plurality of heterogeneous sensor modalities;
extract one or more features specific to a corresponding sensing modality from the sensor data;
determine high-dimensional probability distributions associated with each of the plurality of heterogeneous sensor modalities by processing the one or more extracted features using a tensor network processor;
allocate computational resources to each of the plurality of heterogeneous sensor modalities based on the high-dimensional probability distributions and information gain of each of the plurality of heterogeneous sensor modalities;
temporally align the sensor data using variable-precision timestamps and hardware-accelerated time warping circuits based on the computational resources; and
upon aligning the sensor data, generate fused data representative of a state of an environment by evaluating the high-dimensional probability distributions.
2. The system (102) as claimed in claim 1, wherein the system (102) enables, via a probabilistic routing engine (134), formation of processing pathways to dynamically route the one or more extracted features prior to determining the high-dimensional probability distributions.
3. The system (102) as claimed in claim 2, wherein the probabilistic routing engine (134) comprises a plurality of routing nodes interconnected in a three-dimensional mesh, a routing logic to make routing decisions based on global context, and reinforcement learning modules to update routing preferences based on success signals.
4. The system (102) as claimed in claim 1, wherein each of the one or more domain-specific neuromorphic processing units (126) comprises:
a modality-specific front-end interface (218) for direct sensor connectivity;
a neuromorphic processor (220) comprising spiking neural networks with spatiotemporal pattern encoding module;
a feature extraction module (224), a temporal coherence module (226), and an uncertainty estimation module (228) that are operatively connected to each other; and
a cross-modal interface (222) for processing the extracted features for integration with other modalities.
5. The system (102) as claimed in claim 1, wherein the tensor network (128) comprises:
one or more tensor processing units (234a to 234j) arranged in a reconfigurable mesh (232);
a dedicated hardware for tensor contraction, decomposition, and factorization operations; and
a sampling circuit for direct sampling of sensor data from high-dimensional probability distributions.
6. The system (102) as claimed in claim 1, wherein the system (102) allocates the computational resources to each of the plurality of heterogeneous sensor modalities, by applying an adaptive attention mechanism through an adaptive attention engine (130).
7. The system (102) as claimed in claim 6, wherein the adaptive attention engine (130) uses entropy-based estimation circuits to evaluate the information gain of each of the plurality of heterogeneous sensor modalities in real-time.
8. The system (102) as claimed in claim 6, wherein the adaptive attention engine (130) comprises attention controllers (248a, 248b, 248c, 248n) corresponding to each of the plurality of heterogeneous sensor modalities, and a cross-modal attention arbitrator (246) that coordinates allocation of computational resources across all the heterogeneous sensor modalities
9. The system (102) as claimed in claim 1, further comprising a cross-modal calibration module (132) to maintain relationships between the plurality of heterogeneous sensor modalities, and continuously adapt inter-modality relationships without requiring explicit ground truth.
10. The system (102) as claimed in claim 9, wherein the cross-modal calibration module (132) comprises pairwise calibrators to learn mappings between modality pairs among the plurality of heterogeneous sensor modalities, a consistency enforcer that ensures coherence across all the heterogeneous sensor modalities, and drift compensation circuits that detect and adapt to changing sensor characteristics.
11. The system (102) as claimed in claim 1, wherein the system (102) is configured to detect anomalies through identification of inconsistencies between the plurality of heterogeneous sensor modalities.
12. The system (102) as claimed in claim 1, further comprising a power management subsystem (138) to combine one or more energy sources and provide stable power to the one or more processors.
13. The system (102) as claimed in claim 1, wherein the system (102) is implemented as or in a three-dimensional (3D) stacked integrated circuit package (402) for real-time fusion of heterogeneous sensor data, wherein the 3D stacked integrated circuit package (402) comprises:
one or more silicon dies, wherein the one or more silicon dies are interconnected using Throuh-Silicon Vias (TSVs) for providing high-bandwidth, low-latency inter-layer connections;
a top layer (404) comprising a memory subsystem (214) and a controller logic (410);
a middle layer (406) comprising the NPUs (126) and the tensor network processor; and
a bottom layer (408) comprising analog front-ends (218) for different sensing modalities and a power management subsystem (138).
14. A method (300) for real-time fusion of heterogeneous sensor data, the method (300) comprising:
acquiring, by a system (102), sensor data from a plurality of heterogeneous sensor modalities;
extracting, by the system (102), one or more features specific to a corresponding sensing modality from the sensor data;
determining, by the system (102), high-dimensional probability distributions associated with each of the plurality of heterogeneous sensor modalities by processing the one or more extracted features using a tensor network processor;
allocating, by the system (102), computational resources to each of the plurality of heterogeneous sensor modalities based on the high-dimensional probability distributions and information gain of each of the plurality of heterogeneous sensor modalities;
temporally aligning, by the system (102), the sensor data using variable-precision timestamps and hardware-accelerated time warping circuits based on the computational resources; and
upon aligning the sensor data, generating, by the system (102), fused data representative of a state of an environment by evaluating the high-dimensional probability distributions.