General-Purpose Computing with Soft GPUs on FPGAs

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).