Data representation for dynamic precision in neural network cores

Systems for neural network computation are provided. A neural network processor comprises a plurality of neural cores. The neural network processor has one or more processor precisions per activation. The processor is configured to accept data having a processor feature dimension. A transformation c...

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Hauptverfasser: Myron Flickner, Andrew Stephen Cassidy, Pallab Datta, Hartmut Penner, Dharmendra Modha, Jennifer Klamo, Rathinakumar Appuswamy, Steven Kyle Esser, John Vernon` Arthur, Jun Sawada, Brian Seisho Taba
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creator Myron Flickner
Andrew Stephen Cassidy
Pallab Datta
Hartmut Penner
Dharmendra Modha
Jennifer Klamo
Rathinakumar Appuswamy
Steven Kyle Esser
John Vernon` Arthur
Jun Sawada
Brian Seisho Taba
description Systems for neural network computation are provided. A neural network processor comprises a plurality of neural cores. The neural network processor has one or more processor precisions per activation. The processor is configured to accept data having a processor feature dimension. A transformation circuit is coupled to the neural network processor, and is adapted to: receive an input data tensor having an input precision per channel at one or more features; transform the input data tensor from the input precision to the processor precision; divide the input data into a plurality of blocks, each block conforming to one of the processor feature dimensions; provide each of the plurality of blocks to one of the plurality of neural cores. The neural network processor is adapted to compute, by the plurality of neural cores, output of one or more neural network layers.
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subjects CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
PHYSICS
title Data representation for dynamic precision in neural network cores
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