Self Adaptive Reconfigurable Arrays (SARA): Learning Flexible GEMM Accelerator Configuration and Mapping-space using ML

Abstract

This work demonstrates a scalable reconfigurable accelerator (RA) architecture designed to extract maximum performance and energy efficiency for GEMM workloads. We also present a self-adaptive (SA) unit, which runs a learnt model for one-shot configuration optimization in hardware offloading the software stack thus easing the deployment of the proposed design. We evaluate an instance of the proposed methodology with a 32.768 TOPS reference implementation called SAGAR, that can provide the same mapping flexibility as a compute equivalent distributed system while achieving 3.5X more power efficiency and 3.2X higher compute density demonstrated via architectural and post-layout simulation.