HLS Support for Polymorphic Parallel Memories

2018 IFIP/IEEE International Conference on Very Large Scale Integration (VLSI-SoC)(2018)

引用 0|浏览57
The importance of High-Level Languages in abstracting machine language to enhance productivity has been proved in many sectors, and has recently encouraged the spread of reconfigurable hardware for general purpose computing. At the same time, Field Programmable Gate Arrays (FPGAs) become popular for data-intensive applications, because they promise customized hardware accelerators and achieve high-performance with low power consumption. However, taking advantage of parallel accesses to the local memories of FPGAs remains difficult, as it currently requires application re-engineering. A solution to this challenge is PolyMem, an easy-to-use parallel memory. In this work, we investigate the implementation, integration, and performance of PolyMem for HLS applications. To this end, we present a novel open-source implementation of PolyMem, optimized for the Xilinx Design Suite. We further demonstrate the use of PolyMem for three different case studies, implemented using both the Vivado workflow with a Virtex-7 VC707, and the SDx workflow with a Kintex Ultrascale 3 ADM-PCIE. Finally, we provide a thorough empirical analysis of these three cases studies in terms of latency, hardware resources, and productivity. Our results demonstrate that PolyMem delivers the expected performance, while enhancing productivity at the cost of a small increase in resources.
Field programmable gate arrays,Hardware,Parallel processing,Registers,Productivity,Kernel,Matrix decomposition
AI 理解论文