Automated accelerator generation and optimization with composable, parallel and pipeline architecture

Jason Cong, Peng Wei, Cody Hao Yu, Peng Zhang

Abstract: CPU-FPGA heterogeneous architectures feature flexible acceleration of many workloads to advance computational capabilities and energy efficiency in today's datacenters. This advantage, however, is often overshadowed by the poor programmability of FPGAs. Although recent advances in high-level synthesis (HLS) significantly improve the FPGA programmability, it still leaves programmers facing the challenge of identifying the optimal design configuration in a tremendous design space. In this paper we propose the composable, parallel and pipeline (CPP) microarchitecture as an accelerator design template to substantially reduce the design space. Also, by introducing the CPP analytical model to capture the performance-resource trade-offs, we achieve efficient, analytical-based design space exploration. Furthermore, we develop the AutoAccel framework to automate the entire accelerator generation process. Our experiments show that the AutoAccel-generated accelerators outperform their corresponding software implementations by an average of 72x for a broad class of computation kernels.