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Neural Network Representation Formats

Abstract: Data stream (45) having a representation of a neural network (10) encoded thereinto, the data stream (45) comprising serialization parameter (102) indicating a coding order (104) at which neural network parameters (5 32), which define neuron interconnections (22, 24) of the neural network (10), are encoded into the data stream (45).

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Notices, Deadlines & Correspondence

Patent Information

Application #
Filing Date
25 July 2025
Publication Number
33/2025
Publication Type
INA
Invention Field
COMPUTER SCIENCE
Status
Email
Parent Application

Applicants

FRAUNHOFER-GESELLSCHAFT ZUR FÖRDERUNG DER ANGEWANDTEN FORSCHUNG E.V.
Hansastraße 27c 80686 München, Germany

Inventors

1. MATLAGE, Stefan
Grazer Damm 115 12157 Berlin, Germany
2. HAASE, Paul
c/o Fraunhofer-Institut für Nachrichtentechnik, Heinrich-Hertz-Institut, HHI Einsteinufer 37 10587 Berlin, Germany
3. KIRCHHOFFER, Heiner
c/o Fraunhofer-Institut für Nachrichtentechnik, Heinrich-Hertz-Institut, HHI Einsteinufer 37 10587 Berlin, Germany
4. MÜLLER, Karsten
c/o Fraunhofer-Institut für Nachrichtentechnik, Heinrich-Hertz-Institut, HHI Einsteinufer 37 10587 Berlin, Germany
5. SAMEK, Wojciech
c/o Fraunhofer-Institut für Nachrichtentechnik, Heinrich-Hertz-Institut, HHI Einsteinufer 37 10587 Berlin, Germany
6. WIEDEMANN, Simon
c/o Fraunhofer-Institut für Nachrichtentechnik, Heinrich-Hertz-Institut, HHI Einsteinufer 37 10587 Berlin, Germany
7. MARPE, Detlev
c/o Fraunhofer-Institut für Nachrichtentechnik, Heinrich-Hertz-Institut, HHI Einsteinufer 37 10587 Berlin, Germany
8. SCHIERL, Thomas
c/o Fraunhofer-Institut für Nachrichtentechnik, Heinrich-Hertz-Institut, HHI Einsteinufer 37 10587 Berlin, Germany
9. SÁNCHEZ DE LA FUENTE, Yago
c/o Fraunhofer-Institut für Nachrichtentechnik, Heinrich-Hertz-Institut, HHI Einsteinufer 37 10587 Berlin, Germany
10. SKUPIN, Robert
c/o Fraunhofer-Institut für Nachrichtentechnik, Heinrich-Hertz-Institut, HHI Einsteinufer 37 10587 Berlin, Germany
11. WIEGAND, Thomas
c/o Fraunhofer-Institut für Nachrichtentechnik, Heinrich-Hertz-Institut, HHI Einsteinufer 37 10587 Berlin, Germany

Specification

Description:AS ATTACHED , Claims:I/We Claim:
1. Data stream (45) having a representation of a neural network (10) encoded
thereinto, wherein the data stream (45) comprises hierarchical control data
(400) structured into a 5 sequence (410) of control data portions (420),
wherein the control data portions (420) provide information on the neural
network at increasing details along the sequence of control data portions
(420).
10 2. Apparatus for encoding a representation of a neural network (10) into a data
stream (45), wherein the apparatus is configured to provide the data stream
(45) with hierarchical control data (400) structured into a sequence (410) of
control data portions (420), wherein the control data portions provide
information on the neural network at increasing details along the sequence
15 of control data portions.
3. Apparatus for decoding a representation of a neural network (10) from a
data stream (45), wherein the apparatus is configured to decode from the
data stream (45) hierarchical control data (400) structured into a sequence
20 (410) of control data portions (420), wherein the control data portions
provide information on the neural network at increasing details along the
sequence of control data portions.
4. Apparatus of claim 3, wherein at least some of the control data portions
25 (420) provide information on the neural network which is partially
redundant.
5. Apparatus of claim 3 or claim 4, wherein a first control data portion provides
the information on the neural network by way of indicating a default neural
30 network type implying default settings and a second control data portion
comprises a parameter to indicate each of the default settings.
62
6. Apparatus for performing an inference using a neural network, comprising
an apparatus for decoding a data stream (45) according to any of claims 3 to
5, so as to derive from the data stream (45) the neural network, and
a processor configured to perform 5 the inference based on the neural network.
7. Method for encoding a representation of a neural network into a data stream,
wherein the method comprises providing the data stream with hierarchical
control data structured into a sequence of control data portions, wherein the
10 control data portions provide information on the neural network at
increasing details along the sequence of control data portions.
8. Method for decoding a representation of a neural network from a data
stream, wherein the method comprises decoding from the data stream
15 hierarchical control data structured into a sequence of control data portions,
wherein the control data portions provide information on the neural network
at increasing details along the sequence of control data portions.
9. Computer program for, when executed by a computer, causing the computer
20 to perform the method of claim 7 or claim 8

Documents

Application Documents

# Name Date
1 202518071111-STATEMENT OF UNDERTAKING (FORM 3) [25-07-2025(online)].pdf 2025-07-25
2 202518071111-REQUEST FOR EXAMINATION (FORM-18) [25-07-2025(online)].pdf 2025-07-25
3 202518071111-POWER OF AUTHORITY [25-07-2025(online)].pdf 2025-07-25
4 202518071111-FORM 18 [25-07-2025(online)].pdf 2025-07-25
5 202518071111-FORM 1 [25-07-2025(online)].pdf 2025-07-25
6 202518071111-DRAWINGS [25-07-2025(online)].pdf 2025-07-25
7 202518071111-DECLARATION OF INVENTORSHIP (FORM 5) [25-07-2025(online)].pdf 2025-07-25
8 202518071111-COMPLETE SPECIFICATION [25-07-2025(online)].pdf 2025-07-25
9 202518071111-Proof of Right [13-08-2025(online)].pdf 2025-08-13