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US Patent 10417560 Neural network unit that performs efficient 3-dimensional convolutions

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Is a
Patent
Patent
0

Patent attributes

Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
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Patent Number
104175600
Patent Inventor Names
Kim C. Houck0
G. Glenn Henry0
Date of Patent
September 17, 2019
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Patent Application Number
153660350
Date Filed
December 1, 2016
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Patent Citations
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US Patent 10127494 Neural network crossbar stack
Patent Citations Received
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US Patent 12118451 Deep convolutional network heterogeneous architecture
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US Patent 11836608 Convolution acceleration with embedded vector decompression
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US Patent 11880759 Vector quantization decoding hardware unit for real-time dynamic decompression for parameters of neural networks
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US Patent 11893393 Computational array microprocessor system with hardware arbiter managing memory requests
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US Patent 12073308 Hardware accelerator engine
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US Patent 12086097 Vector computational unit
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US Patent 11531873 Convolution acceleration with embedded vector decompression
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US Patent 11562115 Configurable accelerator framework including a stream switch having a plurality of unidirectional stream links
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Patent Primary Examiner
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Miranda M Huang
0
Patent abstract

A neural network unit convolves a H×W×C input with F R×S×C filters to generate F Q×P outputs. N processing units (PU) each have a register receiving a memory word and a multiplexed-register selectively receiving a memory word or word rotated from an adjacent PU multiplexed-register. The N PUs are logically partitioned as G blocks each of B PUs. The PUs convolve in a column-channel-row order. For each filter column: the N registers read a memory row, each PU multiplies the register and the multiplexed-register to generate a product to accumulate, and the multiplexed-registers are rotated by one; the multiplexed-registers are rotated to align the input blocks with the adjacent PU block. This is performed for each channel. For each filter row, N multiplexed-registers read a memory row for the multiply-accumulations, F column-channel-row-sums are generated and written to the memory, then all steps are performed for each output row.

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