Proteus: Exploiting precision variability in deep neural networks.

•Analyses a set of deep neural networks in terms of required precision per layer.•Describes a method of finding the Pareto frontier in accuracy vs. memory bandwidth.•Shows that required precision varies significantly between layers and networks.•Proposes a variable precision hardware compression method.•Reduces a neural network accelerator’s memory traffic by 43% and energy by 15%.