DeepPump: Multi-pumping deep Neural Networks

摘要

This paper presents DeepPump, a novel approach for generating and optimising hardware designs of deep Convolutional Neural Networks (CNNs) with multi-pumping on FPGA platforms. Multi-pumping [1] is a promising technique to save hardware resource usage by replacing M parallel units with one clocked at M times the global clock rate. DeepPump aims at automatically adopting multi-pumping when generating hardware designs for CNNs. It has three components: a parameterised CNN accelerator architecture that supports multi-pumping, a design model for trade-off analysis related to multi-pumping, and an optimisation flow for improving the architecture based on the design model.