DEF: Differential Encoding of Featuremaps for Low Power Convolutional Neural Network Accelerators

Abstract

As the need for the deployment of Deep Learning applications on edge-based devices becomes ever increasingly prominent, power consumption starts to become a limiting factor on the performance that can be achieved by the computational platforms. A significant source of power consumption for these edge-based machine learning accelerators is off-chip memory transactions. In the case of Convolutional Neural Network (CNN) workloads, a predominant workload in deep learning applications, those memory transactions are typically attributed to the store and recall of feature-maps. There is therefore a need to explicitly reduce the power dissipation of these transactions whilst minimising any overheads needed to do so. In this work, a Differential Encoding of Feature-maps (DEF) scheme is proposed, which aims at minimising activity on the memory data bus, specifically for CNN workloads. The coding scheme uses domain-specific knowledge, exploiting statistics of feature-maps alongside knowledge of the data types commonly used in machine learning accelerators as a means of reducing power consumption. DEF is able to out-perform recent state-of-the-art coding schemes, with significantly less overhead, achieving up to 50% reduction of activity across a number of modern CNNs.