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Proteus: Exploiting Numerical Precision Variability in Deep Neural Networks

P. Judd, J. Albericio, N. Enright Jerger, A. Moshovos, T. Hetherington, T. Aamodt
Department of Electrical and Computer Engineering, University of Toronto, Toronto, Canada
2nd Workshop On Approximate Computing (WAPCO), 2016
@article{judd2016proteus,

   title={Proteus: Exploiting Numerical Precision Variability in Deep Neural Networks},

   author={Judd, Patrick and Albericio, J and Jerger, N Enright and Moshovos, A and Hetherington, T and Aamodt, T},

   year={2016}

}

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This work exploits the tolerance of Deep Neural Networks (DNNs) to reduced precision numerical representations and specifically, their ability to use different representations per layer while maintaining accuracy. This flexibility provides an additional opportunity to improve performance and energy compared to conventional DNN implementations that use a single, uniform representation for all layers throughout the network. This work exploits this property by proposing PROTEUS, a layered extension over existing DNN implementations that converts between the numerical representation used by the DNN execution engines and a shorter, layer specific fixed-point representation when reading and writing data values to memory be it on-chip buffers or off-chip memory. When used with a modified layout of data in memory, PROTEUS can use a simple, low-cost and low energy conversion unit. On five popular DNNs, PROTEUS can reduce data traffic among layers by 41% on average and up to 44% compared to a baseline that uses 16-bit fixed-point representation, while maintaining accuracy within 1% even when compared to a single precision floating-point implementation. When incorporated into a state-of-the-art accelerator PROTEUS improves energy by 14% While maintaining the same performance. When incorporated on a graphics processor PROTEUS improves performance by 1%, energy by 4% and reduces off-chip DRAM accesses by 46%.
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