Energy-Efficiency Evaluation of FPGAs for Floating-Point Intensive Workloads

Author:

Calore Enrico1,Schifano Sebastiano Fabio12

Affiliation:

1. INFN Ferrara, Italy

2. University of Ferrara, Italy

Abstract

In this work we describe a method to measure the computing performance and energy-efficiency to be expected of an FPGA device. The motivation of this work is given by their possible usage as accelerators in the context of floating-point intensive HPC workloads. In fact, FPGA devices in the past were not considered an efficient option to address floating-point intensive computations, but more recently, with the advent of dedicated DSP units and the increased amount of resources in each chip, the interest towards these devices raised. Another obstacle to a wide adoption of FPGAs in the HPC field has been the low level hardware knowledge commonly required to program them, using Hardware Description Languages (HDLs). Also this issue has been recently mitigated by the introduction of higher level programming framework, adopting so called High Level Synthesis approaches, reducing the development time and shortening the gap between the skills required to program FPGAs wrt the skills commonly owned by HPC software developers. In this work we apply the proposed method to estimate the maximum floating-point performance and energy-efficiency of the FPGA embedded in a Xilinx Zynq Ultrascale+ MPSoC hosted on a Trenz board.

Publisher

IOS Press

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. FER: A Benchmark for the Roofline Analysis of FPGA Based HPC Accelerators;IEEE Access;2022

2. FPGA‐based HPC accelerators: An evaluation on performance and energy efficiency;Concurrency and Computation: Practice and Experience;2021-08-22

3. Performance assessment of FPGAs as HPC accelerators using the FPGA Empirical Roofline;2021 31st International Conference on Field-Programmable Logic and Applications (FPL);2021-08

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3