Comprehensive Accelerator-Dataflow Co-design Optimization for Convolutional Neural Networks

Abstract

The design space of possible schedules for mapping a Convolutional Neural Network layer onto a spatial accelerator array, referred as the dataflow, is enormous. The co-design of key architectural parameters (such as number of processing elements, sizes of register files and scratchpad memories) along with the dataflow to optimize the implementation of one or more CNN stages makes the design space explosively larger. Several recent efforts have addressed the design-space exploration problem for CNN accelerators via heuristics or limited search strategies. In this paper we develop the first optimization approach that uses analytical modeling and the solution of constrained nonlinear optimization problems for comprehensive algorithm-architecture co-design optimization. Using the Timeloop accelerator modeling framework, we demonstrate that the new optimization methodology can enable significant improvements over prior accelerator designs for both energy minimization and performance maximization.