Patent application EP21153746A (EP), filed 2021-01-27
Publication number: EP3929822A1
Title: NEUROMORPHIC APPARATUS AND METHOD WITH NEURAL NETWORK
Abstract: [0001]    A processor-implemented neural network implementation method includes: learning each of first layers included in a neural network according to a first method; learning at least one second layer included in the neural network according to a second method; and generating output data from input data by using the learned first layers and the learned at least one second layer.
IPC codes: G06N   3/    04            A I
G06N   3/    08            A I
Claims: number: 1
text: A processor-implemented neural network implementation method, the method comprising:
number: 2
text: The method of claim 1, wherein the first method comprises a method corresponding to unsupervised learning, or a method corresponding to a self-organizing map.
number: 3
text: The method of claim 1 or 2, wherein the second method comprises a method corresponding to supervised leaming, or a method corresponding to back-propagation, or
wherein the first layers comprise convolutional layers and the at least one second layer comprises at least one fully-connected layer.
number: 4
text: The method of one of claims 1 to 3, wherein the learning according to the first method comprises:
number: 5
text: The method of claim 4, wherein the learning of the initial layer comprises:
number: 6
text: The method of claim 5, wherein the generating of the output feature map data of the initial layer comprises:
number: 7
text: The method of one of claims 1 to 6, further comprising learning a next layer of the first layers based on the output feature map data of the initial layer.
number: 8
text: The method of one of claims 1 to 7, wherein the generating of the output data comprises:
number: 9
text: A processor-implemented neural network comprising:
number: 10
text: The neural network of claim 9, wherein the plurality of convolutional layers are trained by a method corresponding to unsupervised learning, or by a method corresponding to a self-organizing map.
number: 11
text: The neural network of claim 9 or 10, wherein the at least one fully-connected layer is trained by a method corresponding to supervised learning, or by a method corresponding to back-propagation.
number: 12
text: A non-transitory computer-readable storage medium storing instructions that, when executed by one or more processors of a neuromorphic neural network implementation apparatus, the instructions configure the one or more processors to:
number: 13
text: The computer-readable storage medium of claim 12, wherein the first method comprises a method corresponding to unsupervised learning, or a method corresponding to a self-organizing map.
number: 14
text: The computer-readable storage medium of claim 12 or 13, wherein the second method comprises a method corresponding to supervised learning, or
a method corresponding to back-propagation, or
wherein the first layers comprise convolutional layers and the at least one second layer comprises at least one fully-connected layer.
number: 15
text: The computer-readable storage medium of one of claims 12 to 14 further comprising an on-chip memory comprising a plurality of cores and storing one or more instructions that, when executed by the processor, configure the processor to:
Office action: search_report, dated 2021-12-29
Rejection reasons: novelty_epc_54
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Final disposition: pending (2021-12-29)