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research-article

General-Purpose Computing with Soft GPUs on FPGAs

Published: 24 January 2018 Publication History

Abstract

Using field-programmable gate arrays (FPGAs) as a substrate to deploy soft graphics processing units (GPUs) would enable offering the FPGA compute power in a very flexible GPU-like tool flow. Application-specific adaptations like selective hardening of floating-point operations and instruction set subsetting would mitigate the high area and power demands of soft GPUs. This work explores the capabilities and limitations of soft General Purpose Computing on GPUs (GPGPU) for both fixed- and floating point arithmetic. For this purpose, we have developed FGPU: a configurable, scalable, and portable GPU architecture designed especially for FPGAs. FGPU is open-source and implemented entirely in RTL. It can be programmed in OpenCL and controlled through a Python API. This article introduces its hardware architecture as well as its tool flow. We evaluated the proposed GPGPU approach against multiple other solutions. In comparison to homogeneous Multi-Processor System-On-Chips (MPSoCs), we found that using a soft GPU is a Pareto-optimal solution regarding throughput per area and energy consumption. On average, FGPU has a 2.9× better compute density and 11.2× less energy consumption than a single MicroBlaze processor when computing in IEEE-754 floating-point format. An average speedup of about 4× over the ARM Cortex-A9 supported with the NEON vector co-processor has been measured for fixed- or floating-point benchmarks. In addition, the biggest FGPU cores we could implement on a Xilinx Zynq-7000 System-On-Chip (SoC) can deliver similar performance to equivalent implementations with High-Level Synthesis (HLS).

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  • (2024)Comparative Analysis of Executing GPU Applications on FPGA: HLS vs. Soft GPU Approaches2024 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)10.1109/IPDPSW63119.2024.00123(634-641)Online publication date: 27-May-2024
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Published In

cover image ACM Transactions on Reconfigurable Technology and Systems
ACM Transactions on Reconfigurable Technology and Systems  Volume 11, Issue 1
Special Section on FCCM 2016 and Regular Papers
March 2018
183 pages
ISSN:1936-7406
EISSN:1936-7414
DOI:10.1145/3178391
  • Editor:
  • Steve Wilton
Issue’s Table of Contents
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 January 2018
Accepted: 01 December 2017
Revised: 01 November 2017
Received: 01 June 2017
Published in TRETS Volume 11, Issue 1

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Author Tags

  1. GPGPU
  2. OpenCL
  3. PYNQ
  4. soft GPUs

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Cited By

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  • (2024)Comparative Analysis of Executing GPU Applications on FPGA: HLS vs. Soft GPU Approaches2024 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)10.1109/IPDPSW63119.2024.00123(634-641)Online publication date: 27-May-2024
  • (2024)Integrating RISC-V SIMT and Scalar Cores: Loosely to Tightly CoupledHigh Performance Computing. ISC High Performance 2024 International Workshops10.1007/978-3-031-73716-9_24(339-353)Online publication date: 14-Dec-2024
  • (2022)Artificial Intelligence for Mass Spectrometry and Nuclear Magnetic Resonance Spectroscopy Using a Novel Data Augmentation MethodIEEE Transactions on Emerging Topics in Computing10.1109/TETC.2021.313137110:1(87-98)Online publication date: 1-Jan-2022
  • (2022)Gaming for Better Psychological Health: A Solution Based on the FPGA Zynq 70002022 8th International Conference on Signal Processing and Communication (ICSC)10.1109/ICSC56524.2022.10009066(602-607)Online publication date: 1-Dec-2022
  • (2022)ICU4SAT: A General-Purpose Reconfigurable Instrument Control Unit Based on Open Source Components2022 IEEE Aerospace Conference (AERO)10.1109/AERO53065.2022.9843414(1-9)Online publication date: 5-Mar-2022
  • (2022)Investigating the reliability impacts of neutron-induced soft errors in aerial image classification CNNs implemented in a softcore SRAM-based FPGA GPUMicroelectronics Reliability10.1016/j.microrel.2022.114738138(114738)Online publication date: Nov-2022
  • (2022)Evaluating low-level software-based hardening techniques for configurable GPU architecturesThe Journal of Supercomputing10.1007/s11227-021-04154-z78:6(8081-8105)Online publication date: 1-Apr-2022
  • (2021)A Manycore Vision Processor for Real-Time Smart CamerasSensors10.3390/s2121713721:21(7137)Online publication date: 27-Oct-2021
  • (2021)Specializing FGPU for Persistent Deep LearningACM Transactions on Reconfigurable Technology and Systems10.1145/345788614:2(1-23)Online publication date: 15-Jul-2021
  • (2021)Neutron-induced Faults on CNN for Aerial Image Classification on SRAM-based FPGA Using Softcore GPU and HLS2021 21th European Conference on Radiation and Its Effects on Components and Systems (RADECS)10.1109/RADECS53308.2021.9954517(1-4)Online publication date: Sep-2021
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