Fast generation of optimized execution plans for parameterizable CNN accelerators: work-in-progress

Abstract

© 2021 ACM.Generating an optimal execution plan for a given convolutional neural network (CNN) and a parameterizable hardware accelerator is a challenge.We present a framework that finds an execution plan that maximizes throughput for a given network and a specific configuration of our parameterizable accelerator. The framework first generates tiled dataflows for each layer, then maps the dataflows to the different independent hardware units using techniques borrowed from traditional list scheduling. Evaluated with a number of different networks and different hardware configurations, the presented framework clearly outperforms existing approaches in terms of speedup or schedule generation time.