POMMEL: Exploring Off-Chip Memory Energy & Power Consumption in Convolutional Neural Network Accelerators

Abstract

Reducing the power and energy consumption of Convolutional Neural Network (CNN) Accelerators is becoming an increasingly popular design objective for both cloud and edge-based settings. Aiming towards the design of more efficient accelerator systems, the accelerator architect must understand how different design choices impact both power and energy consumption. The purpose of this work is to enable CNN accelerator designers to explore how design choices affect the memory subsystem in particular, which is a significant contributing component. By considering high-level design parameters of CNN accelerators that affect the memory subsystem, the proposed tool returns power and energy consumption estimates for a range of networks and memory types. This allows for power and energy of the off-chip memory subsystem to be considered earlier within the design process, enabling greater optimisations at the beginning phases. Towards this, the paper introduces POMMEL, an off-chip memory subsystem modelling tool for CNN accelerators, and its evaluation across a range of accelerators, networks, and memory types is performed. Furthermore, using POMMEL, the impact of various state-of-the-art compression and activity reduction schemes on the power and energy consumption of current accelerations is also investigated.