Laconic Deep Learning Computing

10 May 2018Sayeh SharifyMostafa MahmoudAlberto Delmas LascorzMilos NikolicAndreas Moshovos

We motivate a method for transparently identifying ineffectual computations in unmodified Deep Learning models and without affecting accuracy. Specifically, we show that if we decompose multiplications down to the bit level the amount of work performed during inference for image classification models can be consistently reduced by two orders of magnitude... (read more)

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