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Dynamic Stripes: Exploiting the Dynamic Precision Requirements of Activation Values in Neural Networks

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arxiv 1706.00504 v1 pith:JWJMNK74 submitted 2017-06-01 cs.NE cs.LG

Dynamic Stripes: Exploiting the Dynamic Precision Requirements of Activation Values in Neural Networks

Alberto Delmas , Patrick Judd , Sayeh Sharify , Andreas Moshovos This is my paper
classification cs.NE cs.LG
keywords stripesprecisiondynamicactivationfixed-pointgranularityneuralperformance
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Stripes is a Deep Neural Network (DNN) accelerator that uses bit-serial computation to offer performance that is proportional to the fixed-point precision of the activation values. The fixed-point precisions are determined a priori using profiling and are selected at a per layer granularity. This paper presents Dynamic Stripes, an extension to Stripes that detects precision variance at runtime and at a finer granularity. This extra level of precision reduction increases performance by 41% over Stripes.

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