DEF: Differential Encoding of Featuremaps for Low Power Convolutional Neural Network Accelerators
Asia and South Pacific Design Automation Conference(2021)
摘要
ABSTRACTAs 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.
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关键词
Power Optimisation, Activity Coding, Neural Networks
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