Spatio-Temporal Optimization of Deep Neural Networks for Reconfigurable FPGA SoCs

Abstract

This article proposes a technique for optimizing the timing performance and the resource consumption of hardware accelerators for deep neural network (DNN) inference on FPGA-based system-on-chips (SoC). When required, the accelerators are decomposed into chunks, each exploiting at best the available FPGA area, and dynamic partial reconfiguration (DPR) is leveraged to schedule such chunks at run-time. To this end, the article presents accurate models of the resource consumption and timing of DNN accelerators provided by the Xilinx FINN framework. The models are then used to formulate an optimization problem that computes the optimal decomposition of DNN accelerators (and their configuration) by minimizing the inference time while ensuring area constraints on the FPGA. Experimental results on Zynq-7000 platforms demonstrate that the proposed technique provides consistent improvements with respect to both stock configurations of the accelerators and other configurations that can be obtained with a static FPGA allocation.