Description:TECHNICAL FIELD
[0001] The present disclosure relates, in general, to computing systems, and more specifically, relates to a hybrid hardware synergizing field-programmable gate array (FPGA) and graphics processing unit (GPU) capabilities for high-performance multi-channel data processing.

BACKGROUND
[0002] In the realm of high-performance computing, the continuous evolution of technology has catalysed the need for integrated architectures capable of seamlessly addressing the complex demands of data acquisition, signal processing, and machine learning applications.
[0003] A few examples of such systems are recited in an IEEE paper published in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) October 25-29, 2020, Las Vegas, NV, USA entitled “Basic Implementation of FPGA-GPU Dual SoC Hybrid Architecture for Low-Latency Multi-DOF Robot Motion Control” describes basic implementation of an embedded controller board based on a hybrid architecture equipped with an Intel FPGA SoC and an NVIDIA GPU SoC. An embedded distributed network involving motor drivers or other embedded boards is constructed with a low-latency optical transmission link. The central controller for high-level motion planning is connected via Gigabit Ethernet. The controller board with the hybrid architecture provides lower latency feedback control performance.
[0004] The computing performance of the FPGA SoC, the GPU SoC, and the central controller is evaluated by the computation time of matrix multiplication. Then, the total feedback latency is estimated to show the performance of the hybrid architecture. Science Direct journals and books an article entitled “A Hybrid GPU-FPGA-based Computing Platform for Machine Learning” explains the performance improvement of benchmark LeNet-5 machine learning model in two different architectures one with CPU and GPU and the other with FPGA and GPU proves that the second model has five times faster performance in implementing the standard algorithm.
[0005] Another example is recited in a US Patent US10552935B2 entitled “Direct communication between GPU and FPGA components” discloses hardware that may include a Graphics Processing Unit (GPU) and a Field Programmable Gate Array (FPGA). The system may further include a bus interface that is external to the FPGA, and that is configured to transfer data directly between the GPU and the FPGA without storing the data in the memory of a central processing unit (CPU) as an intermediary operation.
[0006] Another example is recited in Chinese Patent CN108804376B entitled “Small heterogeneous processing system based on GPU and FPGA” discloses a heterogeneous processing system based on a GPU and an FPGA, wherein two GPU SOC modules are connected to an FPGA module through PCIE X4 and used as two PCIE slave devices of the FPGA for data interaction; the two GPU SOC modules are connected to the gigabit Ethernet switching module through an internally arranged Ethernet interface; the FPGA module is connected to the host through PCIE X4, so that the two GPU SOC modules realize data interaction with the host; the gigabit Ethernet switching module is connected to the host through a network interface of the CPCIE, so that gigabit Ethernet data interaction between the two GPU SOC modules and the host is realized; the method has the characteristics of low power consumption, strong operation performance, simple structure, flexible design, simple development, cascade connection of heterogeneous systems and multiple applicable scenes.
[0007] Another example is recited in Chinese Patent CN104820207B entitled “Real-time correlator based on FPGA, GPU and CPU mixed architecture” discloses a kind of real-time correlator based on FPGA, GPU and CPU mixed architecture, including:Signal sampling module, with N number of passage for being respectively used to sampling N roads signal;Signal preprocessing module based on FPGA, the signal for being set to sample passage each described is mixed successively, LPF and packing are processed;Unpacking module based on CPU, is set to unpack the signal by signal pre-processing module packing treatment;Signal transacting and related operation module based on GPU, are set to carry out multiphase filtering, Fourier transform, related operation and integration successively to the N roads signal after unpacking respectively;And the control module based on CPU, being on the one hand set to control signal pre-treatment module and signal transacting and related operation module carries out respective handling, is on the other hand set to store and show by the signal of Integral Processing. The present invention both overcomes FPGA logic resource-constrained cannot dilatation on a large scale barrier, and solves the problems, such as that CPU computing capabilities are limited again.
[0008] Although multiple systems exist today, these systems suffer from significant drawbacks. Therefore, it is desired to overcome the drawbacks, shortcomings, and limitations associated with existing solutions, and develop a system that integrates dedicated data conversion capabilities from the data converter card, seamlessly combines dynamic signal processing abilities of the FPGA and incorporates advanced machine learning capabilities of the GPU SOM.

OBJECTS OF THE PRESENT DISCLOSURE
[0009] An object of the present disclosure relates, in general, to computing systems, and more specifically, relates to a hybrid hardware synergizing FPGA and GPU capabilities for high-performance multi-channel data processing.
[0010] Another object of the present disclosure is to provide a system that integrates dedicated data conversion capabilities from the data converter card, seamlessly combines dynamic signal processing abilities of the FPGA and incorporates advanced machine learning capabilities of the GPU SOM.
[0011] Another object of the present disclosure is to provide a system that adopts a modular structure that ensures scalability and adaptability enabling the architecture to cater to a broad spectrum of signal processing and machine learning requirements.
[0012] Another object of the present disclosure is to provide a versatile system, addressing a wide array of applications suitable for traditional sensor-based data acquisition tasks, capable of handling sophisticated high-level machine learning computations.
[0013] Another object of the present disclosure is to provide a system that positions itself at the forefront in integrated high-performance computing, offers flexibility to adapt to cutting-edge technological advancements and addresses diverse needs, making it relevant for evolving applications.
[0014] Another object of the present disclosure is to provide a system that provides a meticulous combination of dedicated data conversion, FPGA-based signal processing, and GPU-accelerated machine learning to facilitate efficiency, adaptability, and computational power.
[0015] Yet another object of the present disclosure is to provide a system that provides a robust platform for advancing the frontiers of high-performance computing applications, offers increased computational power and efficiency for demanding tasks and positions itself as a cutting-edge solution for intensive computing requirements.

SUMMARY
[0016] The present disclosure relates, in general, to computing systems, and more specifically, relates to a hybrid hardware synergizing FPGA and GPU capabilities for high-performance multi-channel data processing. The main objective of the present disclosure is to overcome the drawbacks, limitations, and shortcomings of the existing system and solution, by providing a system and method in the field of data acquisition, signal processing, and machine learning.
[0017] The present disclosure presents a solution in the form of a hybrid architecture, strategically designed to excel in each of these domains. Central to this framework is a Versa Module Europa (VME) bus standard hardware platform, housing three pivotal modules such as a data converter card with analog-to-digital converters (ADC) and digital-to-analog converters (DAC), a field-programmable gate array (FPGA), and an NVIDIA Graphics Processing Unit (GPU) system on module (SOM). At the forefront of this architecture is the data converter card, serving as the initial interface for gathering information from connected sensors. This crucial component is equipped with a Serializer/Deserializer (SerDes) or JESD204B interface, a technological cornerstone enabling flexible and programmable sampling rates. This adaptability facilitates efficient data transmission to the FPGA, forming the essential groundwork for subsequent processing stages.
[0018] Further, the FPGA module assumes a central role, taking charge of control, data acquisition, and the execution of signal processing algorithms. Operating in real-time, the FPGA serves as a dynamic orchestrator, ensuring the efficient processing of acquired data. It intelligently preprocesses and filters the incoming data, optimizing it for subsequent high-performance computations.
[0019] The hybrid architecture is the seamless integration of a high-end NVIDIA GPU SOM. This strategic amalgamation introduces a new dimension to the system, empowering it with advanced machine-learning capabilities and the ability to execute complex algorithms. The FPGA acts as a sophisticated intermediary, rigorous pre-processing and refining the data before seamlessly transmitting it to the GPU SOM. Leveraging the parallel processing capabilities inherent in GPUs, this architecture achieves unprecedented efficiency in the execution of intricate machine-learning algorithms and high-performance computations.
[0020] The present disclosure lies in its holistic and comprehensive approach, bringing together the strengths of dedicated data conversion, FPGA-based signal processing, and GPU-accelerated machine learning. The modular design of the system ensures scalability, adaptability, and a delicate balance between real-time processing and the execution of sophisticated algorithms. This hybrid system emerges as a versatile solution, poised to address the diverse demands of applications ranging from sensor-based data acquisition to high-level machine learning tasks. As such, it stands at the forefront of cutting-edge technological advancements, offering a robust and integrated platform to propel innovation in the field of high-performance computing.
[0021] 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 like components.
BRIEF DESCRIPTION OF THE DRAWINGS
[0022] The following drawings form part of the present specification and are included to further illustrate aspects of the present disclosure. The disclosure may be better understood by reference to the drawings in combination with the detailed description of the specific embodiments presented herein.
[0023] FIG. 1 illustrates a basic architecture of the proposed hybrid architecture with data convertor card, FPGA and GPU, in accordance with an embodiment of the present disclosure.
[0024] FIG. 2A illustrates the data flow model implemented for low-speed data requirements in the hybrid architecture, in accordance with an embodiment of the present disclosure.
[0025] FIG. 2B illustrates the data flow model implemented for high-speed data requirements in the hybrid architecture, in accordance with an embodiment of the present disclosure.
[0026] FIG. 3 illustrates the placement of different modules in a VME standard-size B hardware and the communications between the modules, in accordance with an embodiment of the present disclosure.
[0027] FIG. 4 illustrates an exemplary flow chart of a method for multi-channel data processing, in accordance with an embodiment of the present disclosure.

DETAILED DESCRIPTION
[0028] The following is a detailed description of embodiments of the disclosure depicted in the accompanying drawings. The embodiments are in such detail as to clearly communicate the disclosure. If the specification states a component or feature “may”, “can”, “could”, or “might” be included or have a characteristic, that particular component or feature is not required to be included or have the characteristic.
[0029] As used in the description herein and throughout the claims that follow, the meaning of “a,” “an,” and “the” includes plural reference unless the context clearly dictates otherwise. Also, as used in the description herein, the meaning of “in” includes “in” and “on” unless the context clearly dictates otherwise.
[0030] The present disclosure introduces a hybrid architecture designed for high-performance data acquisition, signal processing, and machine learning applications. The system is built upon a VME bus standard size B hardware platform, comprising three main modules such as a data converter card equipped with Analog-to-Digital Converters (ADC) and Digital-to-Analog Converters (DAC), a Field-Programmable Gate Array (FPGA), and a Graphics Processing Unit (GPU) System on Module (SOM). The data converter card serves as the initial interface for sensing information from connected sensors. It incorporates a Serializer/Deserializer (SerDes) interface, enabling flexible and programmable sampling rates for efficient data transmission to the FPGA. The FPGA module takes charge of control, data acquisition, and signal processing algorithms, ensuring real-time processing of acquired data. A distinctive feature of the proposed architecture lies in the seamless integration of high-end GPU SOM for advanced machine learning and algorithmic computations. The FPGA intelligently preprocesses and filters data before forwarding it to the GPU SOM, where complex machine learning algorithms and high-performance computations are executed, harnessing the parallel processing capabilities of the GPU. The of this architecture performs a comprehensive approach, combining the strengths of dedicated data conversion, FPGA-based signal processing, and GPU-accelerated machine learning. The modular structure ensures scalability, adaptability, and a balance between real-time processing and sophisticated algorithmic execution. The hybrid system addresses the demands of diverse applications, from sensor-based data acquisition to high-level machine learning tasks, making it a versatile solution for cutting-edge technological advancements.
[0031] In an aspect, the present disclosure relates to a system for multi-channel data processing, the system includes a pluggable data converter unit configured to collect a set of data from a plurality of sensors. A field-programmable gate array (FPGA) acquires the set of data from the data converter unit and executes real-time control and signal processing on the acquired data; and a pluggable Graphics Processing Unit (GPU) configured to receive the processed data from the FPGA through a Peripheral Component Interconnect Express (PCIe) interface, facilitating machine learning tasks.
[0032] In another aspect, the data converter unit is equipped with Analog-to-Digital Converters (ADC) and Digital-to-Analog Converters (DAC) configured to convert analog signals to digital signals and vice versa. The data converter unit is identified as a pluggable multichannel converter system, allowing for the customization of analog bandwidth, sampling rates, and digital interface features based on the specific requirements of corresponding applications.
[0033] In another aspect, the data converter unit with the ADC/DAC is connected to a carrier card using Versa Module Europa (VITA57) standard Field Mezzanine Card (FMC) connector.
[0034] In another aspect, the data converter card digitizes data according to user-defined settings and establishes communication with the FPGA through any or a combination of, Serializer/Deserializer (SerDes) or Jitter-Enhanced Serial Data Interface standard (JESD204B) utilizing various pluggable boards.
[0035] In another aspect, the FPGA is positioned on the carrier card, wherein the carrier card is designed according to the VME SIZEB standard.
[0036] In another aspect, the transfer of the set of data between the FPGA and GPU SOM is chosen based on requirements of speed and volume of data transfer. In one aspect, the FPGA utilizes DMA transfer for direct data transfer from a primary memory of the FPGA to a primary memory of GPU. In another aspect, the FPGA utilizes DMA transfer for direct data transfer from a secondary memory of FPGA to a secondary memory of GPU, wherein the secondary memory of FPGA and GPU are DDR4 8GB RAM.
[0037] In another aspect, the transfer of the set of data between the FPGA and GPU utilizes Ethernet when the available bandwidth through PCIe is deemed insufficient, wherein the Ethernet is selected from a 10G Ethernet connection to facilitate the transfer of the set of data. The present disclosure can be described in enabling detail in the following examples, which may represent more than one embodiment of the present disclosure.
[0038] The advantages achieved by the system of the present disclosure can be clear from the embodiments provided herein. The system seamlessly integrates dedicated data conversion capabilities from the data converter card, harnesses the dynamic signal processing abilities of the FPGA, and incorporates advanced machine learning capabilities of the GPU SOM. Its modular structure ensures scalability and adaptability, catering to a broad spectrum of signal processing and machine learning needs. The versatile system excels in traditional sensor-based data acquisition tasks and handles sophisticated high-level machine learning computations. Positioned at the forefront of integrated high-performance computing, it remains flexible to adapt to cutting-edge technological advancements, making it relevant for evolving applications. The meticulous combination of dedicated data conversion, FPGA-based signal processing, and GPU-accelerated machine learning enhances efficiency, adaptability, and computational power. Serving as a robust platform, this system advances the frontiers of high-performance computing applications, providing increased computational power and efficiency for demanding tasks, and positioning itself as a cutting-edge solution for intensive computing requirements. The description of terms and features related to the present disclosure shall be clear from the embodiments that are illustrated and described; however, the invention is not limited to these embodiments only. Numerous modifications, changes, variations, substitutions, and equivalents of the embodiments are possible within the scope of the present disclosure. Additionally, the invention can include other embodiments that are within the scope of the claims but are not described in detail with respect to the following description.
[0039] FIG. 1 illustrates a basic architecture of the proposed hybrid architecture with data convertor card, FPGA and GPU, in accordance with an embodiment of the present disclosure.
[0040] Regarding FIG. 1, hybrid system 100 can be rigorously engineered for high-performance data acquisition, signal processing, and machine learning applications. The system 100 can include three main modules integrated into a VMEbus standard size B hardware platform: The three main modules can include a data converter card 104, a field-programmable gate array (FPGA) 108, and a NVIDIA graphics processing unit (GPU) system on module (SOM) 112. The FIG.1 depicts the basic hybrid architecture with the important modules explained below.
[0041] The data converter card 104 begins with the collection of multi-channel data input received from one or more sensors 102 to the data converter card 104, serving as the initial point of contact with connected sensors 102. The data converter card 104 is equipped with high-performance Analog-to-Digital Converters (ADC) and Digital-to-Analog Converters (DAC), and captures raw sensor data with precision. The integration of a Serializer/Deserializer (SerDes) or JESD204B 106 interface on the data converter card 104. This feature introduces adaptability through flexible and programmable sampling rates, optimizing the transmission of sensed information to the subsequent processing stages.
[0042] In an embodiment, the data converter card 104 is configured to be swappable and includes ADCs and DACs with a digital interface. The data converter card 104 functions as a pluggable module, facilitating ease of replacement. The multi-channel data converter is configurable with analog and digital features tailored to specific application requirements. This structure ensures adaptability to diverse sensor needs, offering a flexible solution for data acquisition across a spectrum of sensing technologies.
[0043] The FPGA module 108 assumes a central role in the architecture, orchestrating control, data acquisition, and the execution of real-time signal processing algorithms. Its dynamic nature allows for on-the-fly adaptability, crucial for handling diverse signal processing tasks efficiently. The FPGA module 108 serves as a dedicated real-time processing unit, executing control functions, data acquisition, and signal processing algorithms. The FPGA-based processing ensures low-latency and high-throughput capabilities, optimizing the efficiency of the overall system.
[0044] Intelligent preprocessing aspect of the FPGA's functionality is its intelligent preprocessing capabilities. This involves filtering and refinement of incoming data before its onward transmission to the GPU SOM 112. This preprocessing step is instrumental in optimizing data for subsequent high-performance computations using standard PCIe GEN3 four lane interface 110.
[0045] The architecture achieves a seamless integration of a high-end NVIDIA GPU SOM 112. This integration represents a fusion of traditional signal processing capabilities with advanced machine learning and algorithmic computations, tapping into the parallel processing prowess of GPUs. The integration of an NVIDIA GPU SOM 112 introduces a high-performance parallel processing unit specifically designed for machine learning tasks. The collaboration between FPGA 108 and GPU 112 facilitates a two-tier processing approach, allowing the FPGA to preprocess data before handing it over to the GPU for intricate machine learning computations.
[0046] The GPU SOM 112 becomes the powerhouse for executing complex machine learning algorithms and high-performance computations. Acting in concert with the FPGA 108, it forms a synergistic partnership that capitalizes on the parallel processing capabilities inherent in GPUs, resulting in a significant leap in computational efficiency.
[0047] The utilization of the VMEbus standard sizeB provides a robust and widely accepted hardware platform, ensuring compatibility and interoperability. This integration enhances the system's reliability and ease of integration into existing setups, making it suitable for diverse applications.
[0048] The hybrid architecture's modular design and versatility make it applicable to a wide array of use cases. From basic sensor data acquisition to complex machine learning applications, the system can adapt to the requirements of diverse industries, including healthcare, automotive, aerospace and more. The modular nature of the architecture allows for configurability to meet evolving demands. Users can easily configure the system's capabilities by upgrading existing components, ensuring the longevity and relevance of the solution in the face of technological advancements.
[0049] The adaptation of FPGA for real-time processing and NVIDIA GPU for high-end algorithmic computations establishes a balanced processing approach. This ensures optimal resource utilization, avoiding bottlenecks and providing an efficient solution for applications demanding both real-time responsiveness and sophisticated algorithmic execution.
[0050] Thus, the present invention overcomes the drawbacks, shortcomings, and limitations associated with existing solutions, and provides a system that seamlessly integrates dedicated data conversion capabilities from the data converter card, harnesses the dynamic signal processing abilities of the FPGA, and incorporates advanced machine learning capabilities of the GPU SOM. Its modular structure ensures scalability and adaptability, catering to a broad spectrum of signal processing and machine learning needs. The versatile system excels in traditional sensor-based data acquisition tasks and handles sophisticated high-level machine learning computations. Positioned at the forefront of integrated high-performance computing, it remains flexible to adapt to cutting-edge technological advancements, making it relevant for evolving applications. The meticulous combination of dedicated data conversion, FPGA-based signal processing, and GPU-accelerated machine learning enhances efficiency, adaptability, and computational power. Serving as a robust platform, this system advances the frontiers of high-performance computing applications, providing increased computational power and efficiency for demanding tasks and positioning itself as a cutting-edge solution for intensive computing requirements.
[0051] FIG. 2A shows the data flow model implemented for low-speed data requirements in the hybrid architecture, in accordance with an embodiment of the present disclosure.
[0052] FIG. 2A and FIG.2B illustrate the data control and the flow between the modules. Two different approaches are being implemented and the selection of the data transfer is done depending on the data rates to be transferred. The definition of data rates depends on the FPGA resources and the max clock that has been implemented in the Register-Transfer Level (RTL). The FIG.2A shows the data flow from data convertors 104 to FPGA 108, from primary memory 202-1 of FPGA 108 to primary memory 204-1 of GPU 112 when the data rates are less. The data transfer is common in both the implementation through PCIe 110.
[0053] FIG. 2B illustrates the data flow model implemented for high-speed data requirements in the hybrid architecture, in accordance with an embodiment of the present disclosure. The FIG.2B depicts the data flow when the data rates are high. A direct transfer protocol is implemented using Xilinx Direct Memory Access (XDMA) ipcore from Xilinx. Data is transferred between the secondary memory 202-2 of FPGA 108 to secondary memory 204-2 of GPU 112. DDR4 is the secondary memory in the above-implemented system.
[0054] FIG. 3 illustrates the placement of different modules in a VME standard sizeB hardware and the communications between the modules, in accordance with an embodiment of the present disclosure.
[0055] FIG.3 depicts the top view of the implemented hardware on a VME sizeB standard board. The hardware is centered around a single carrier card, conforming to the VME standard, and designed with Size B dimensions features first and second connectors (302-1, 302-2) located on the backside of the board.
[0056] Positioned on the carrier board are key components, including one FPGA 108, one NVIDIA SOM module 112, and the data converter card 104 with an FPGA Mezzanine Card (FMC) standard connector. The data converter card 104, serving as an add-on, seamlessly interfaces with the carrier board through the FMC standard connector. The communication architecture is established using high-speed SerDes or JESD204B 304 between the FPGA 108 and the data converter card 104. The carrier board, following VME standards, is equipped with a diverse array of interfaces directly linked to the FPGA 108.
[0057] These interfaces encompass JTAG (306-1), Ethernet 10G (306-2), DDR4 306-3, RS422 306-4, and RS232 306-5, providing comprehensive connectivity options. Additionally, the carrier board features a connector for integrating the NVIDIA SOM module 112, forming a PCIe connection 110 with the FPGA 108. The NVIDIA SOM module 112 enhances the system's capabilities with interfaces including High-Definition Multimedia Interface (HDMI) 308-1, Universal Serial Bus (USB) 3.0 308-2, USB 308-3, and Ethernet 1G 308-4. This integrated and standardized hardware design facilitates efficient communication and robust interoperability for a range of applications.
[0058] The FPGA 108 processes multichannel data and directs the output to the DDR4 memory. To facilitate the transfer of this processed data, a direct Xilinx Direct Memory Access (XDMA) IP core 310 is employed. The XDMA IP 310 core efficiently manages the data transfer from the DDR4 memory 306-3 to the NVIDIA SOM 112 using the PCIe interface 110. This configuration ensures a high-throughput and low-latency data transfer mechanism. The FPGA 108, with its parallel processing capabilities, handles the multichannel data processing, and stores the results in the DDR4 memory 306-3.
[0059] The XDMA IP core 310 takes charge of efficiently orchestrating the data movement from the DDR4 memory 306-3 to the NVIDIA SOM 112 via the PCIe link 110. Utilizing the Xilinx IP core streamlines the data transfer process, optimizing the overall performance of the system. The PCIe interface 110, known for its high-speed and reliable data transfer capabilities, ensures that the processed data reaches the NVIDIA SOM 112 swiftly and seamlessly, enhancing the efficiency of the entire data processing pipeline.
[0060] FIG. 4 illustrates an exemplary flow chart of a method for multi-channel data processing, in accordance with an embodiment of the present disclosure.
[0061] Referring to FIG. 4, the method 400 includes at block 402, the pluggable data converter unit configured to collect a set of data from a plurality of sensors. At block 404, the field-programmable gate array (FPGA) acquires the set of data from the data converter unit and executes real-time control and signal processing on the acquired data and at block 406 a pluggable Graphics Processing Unit (GPU) is configured to receive the processed data from the FPGA through a Peripheral Component Interconnect Express (PCIe) interface, facilitating machine learning tasks.
[0062] The present disclosure distinctiveness lies in its holistic approach, seamlessly blending the dedicated data conversion capabilities of the data converter card, the dynamic signal processing prowess of the FPGA, and the advanced machine learning capabilities of the GPU SOM. The modular design ensures scalability and adaptability, enabling the architecture to cater to a spectrum of signal processing and machine learning requirements. This flexibility positions the system as a versatile solution capable of addressing the diverse needs of applications ranging from sensor-based data acquisition to high-level machine-learning tasks. The present disclosure addresses a wide array of applications, making it a versatile solution for cutting-edge technological advancements. Its applicability spans from traditional sensor-based data acquisition tasks to more sophisticated high-level machine learning computations, positioning it at the forefront of innovation in the field of integrated high-performance computing.
[0063] In essence, the present disclosure represents a paradigm shift in integrated computing architectures. Its meticulous combination of dedicated data conversion, FPGA-based signal processing, and GPU-accelerated machine learning introduces a new era of efficiency, adaptability, and computational power, offering a robust platform for advancing the frontiers of high-performance computing applications.
[0064] It will be apparent to those skilled in the art that the system 100 of the disclosure may be provided using some or all of the mentioned features and components without departing from the scope of the present disclosure. While various embodiments of the present disclosure have been illustrated and described herein, it will be clear that the disclosure is not limited to these embodiments only. Numerous modifications, changes, variations, substitutions, and equivalents will be apparent to those skilled in the art, without departing from the spirit and scope of the disclosure, as described in the claims.

ADVANTAGES OF THE PRESENT INVENTION
[0065] The present disclosure provides a system that integrates dedicated data conversion capabilities from the data converter card, seamlessly combines dynamic signal processing abilities of the FPGA and incorporates advanced machine learning capabilities of the GPU SOM.
[0066] The present disclosure provides a system that adopts a modular structure that ensures scalability and adaptability enabling the architecture to cater to a broad spectrum of signal processing and machine learning requirements.
[0067] The present disclosure provides a versatile system, addressing a wide array of applications suitable for traditional sensor-based data acquisition tasks, capable of handling sophisticated high-level machine learning computations.
[0068] The present disclosure provides a system that positions itself at the forefront in integrated high-performance computing, offers flexibility to adapt to cutting-edge technological advancements and addresses diverse needs, making it relevant for evolving applications.
[0069] The present disclosure provides a system that provides a meticulous combination of dedicated data conversion, FPGA-based signal processing, and GPU-accelerated machine learning to facilitate efficiency, adaptability, and computational power.
[0070] The present disclosure provides a system that provides a robust platform for advancing the frontiers of high-performance computing applications, offers increased computational power and efficiency for demanding tasks and positions itself as a cutting-edge solution for intensive computing requirements.

, Claims:1. A system (100) for multi-channel data processing, the system comprising:
a pluggable data converter unit (104) configured to collect a set of data from a plurality of sensors (102);
a field-programmable gate array (FPGA) (108) acquires the set of data from the data converter unit (104) and executes real-time control and signal processing on the acquired data; and
a pluggable graphics processing unit (GPU) (112) configured to receive the processed data from the FPGA (108) through a peripheral component interconnect express (PCIe) interface (110), facilitating machine learning tasks.

2. The system claimed in claim 1, wherein the data converter unit (104) is equipped with Analog-to-Digital Converters (ADC) and Digital-to-Analog Converters (DAC) configured to convert analog signals to digital signals and vice versa, wherein the data converter unit (104) is identified as a pluggable multichannel converter system, allowing for customization of analog bandwidth, sampling rates, and digital interface features based on specific requirements of corresponding applications.

3. The system claimed in claim 1, wherein the data converter unit (104) with the ADC/DAC is connected to a carrier card using Versa Module Europa (VITA57) standard Field Mezzanine Card (FMC) connector.

4 The system claimed in claim 1, wherein the data converter card digitizes data according to user-defined settings and establishes communication with the FPGA through any or a combination of, Serializer/Deserializer (SerDes) or Jitter-Enhanced Serial Data Interface standard (JESD204B) utilizing various pluggable boards.
5. The system claimed in claim 1, wherein the FPGA (108) is positioned on the carrier card, wherein the carrier card is designed according to the VME SIZE B standard.

6. The system claimed in claim 1, wherein transfer of the set of data between the FPGA (108) and GPU (112) is chosen based on requirements of speed and volume of data transfer.

7. The system claimed in claim 1, wherein the FPGA (108) utilizes direct memory access (DMA) transfer for direct data transfer from a primary memory (202-1) of the FPGA (108) to a primary memory (204-1) of the GPU (112).

8. The system claimed in claim 1, wherein the FPGA (108) utilizes DMA transfer for direct data transfer from a secondary memory (202-2) of the FPGA (108) to a secondary memory (204-2) of the GPU (112), wherein the secondary memory of the FPGA and the GPU is double data rate (DDR4) 8GB random-access memory (RAM).

9. The system claimed in claim 1, wherein the transfer of the set of data between the FPGA (108) and the GPU (112) utilizes Ethernet when the available bandwidth through the PCIe interface (110) is deemed insufficient, wherein the Ethernet selected from a 10G Ethernet connection to facilitate the transfer of the set of data.

10. A method (400) for multi-channel data processing, and machine learning, the method system comprising:
collecting (402), at a pluggable data converter unit, a set of data from a plurality of sensors (102);
acquiring (404), by a field-programmable gate array (FPGA), the set of data from the data converter unit and executing real-time control and signal processing on the acquired data; and
receiving (406), at a pluggable graphics processing unit (GPU), the processed data from the FPGA through a PCIe interface, facilitating machine learning tasks.