A generator of numerically-tailored and high-throughput accelerators for batched GEMMs
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Document typeConference report
Defense date2022
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Rights accessOpen Access
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Abstract
We propose a hardware generator of GEMM accelerators. Our generator produces vendor-agnostic HDL describing highly customizable systolic arrays guided by accuracy and energy efficiency goals. The generated arrays have three main novel aspects. First, the accelerators handle a large variety of computer number formats using intermediate representations based on our Sign Scale Significand (S3) format. Second, the processing elements perform all intermediate dot-product arithmetic operations required by the GEMM kernel without any intermediate rounding, which makes it possible to deliver better energy efficiency than state-of-the-art approaches while offering more accuracy and reproducible results. Third, our accelerators feature the Half-Speed Sink Down (HSSD) mechanism, which maximizes the overlap of host-accelerator data transfers with GEMM computations.We evaluate our automatically generated designs in a cutting-edge setup composed of a POWER9 host, CAPI (Coherent Accelerator Processor Interface) link, and a Virtex Ultrascale Plus FPGA. Arrays can operate at the speed of the link and saturate it to reach a 13GB/s throughput. Our fine-grain customization approach allows to cover a wide range of accuracy versus efficiency scenarios and can reach 0.65GOps/s/W while producing 1024 accurate bits or 148.7GOps/s/W with 6 accurate bits. Our configurations achieve up to 1613GOps/s system performance and power efficiencies of up to 240GOps/s/W for the FPGA. This automatic generator is the first being able to produce such a variety of designs. We improve the single-precision energy efficiency of state-of-the-art FPGA GEMM accelerators by 1.86×.
CitationLedoux, L.; Casas, M. A generator of numerically-tailored and high-throughput accelerators for batched GEMMs. A: IEEE Symposium on Field Programmable Custom Computing Machines. "2022 IEEE 30th International Symposium on Field-Programmable Custom Computing Machines, FCCM 2022: 15-18 May, 2022, New York, NY, USA: proceedings". Institute of Electrical and Electronics Engineers (IEEE), 2022, ISBN 978-1-6654-8332-2. DOI 10.1109/FCCM53951.2022.9786164.
ISBN978-1-6654-8332-2
Publisher versionhttps://ieeexplore.ieee.org/document/9786164
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