Affiliation:
1. University of British Columbia, Vancouver, British Columbia, Canada
2. University of Sydney, Sydney, Australia, NSW
Abstract
Recent years have seen an explosion of machine learning applications implemented on
Field-Programmable Gate Arrays
(FPGAs)
. FPGA vendors and researchers have responded by updating their fabrics to more efficiently implement machine learning accelerators, including innovations such as enhanced
Digital Signal Processing (DSP)
blocks and hardened systolic arrays. Evaluating architectural proposals is difficult, however, due to the lack of publicly available benchmark circuits.
This paper addresses this problem by presenting an open-source benchmark circuit generator that creates realistic DNN-oriented circuits for use in FPGA architecture studies. Unlike previous generators, which create circuits that are agnostic of the underlying FPGA, our circuits explicitly instantiate embedded blocks, allowing for meaningful comparison of recent architectural proposals without the need for a complete inference
computer-aided design (CAD)
flow. Our circuits are compatible with the VTR CAD suite, allowing for architecture studies that investigate routing congestion and other low-level architectural implications.
In addition to addressing the lack of machine learning benchmark circuits, the architecture exploration flow that we propose allows for a more comprehensive evaluation of FPGA architectures than traditional static benchmark suites. We demonstrate this through three case studies which illustrate how realistic benchmark circuits can be generated to target different heterogeneous FPGAs.
Publisher
Association for Computing Machinery (ACM)
Reference52 articles.
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3. Hamamu: Specializing FPGAs for ML Applications by Adding Hard Matrix Multiplier Blocks
4. Shortcut Mining: Exploiting Cross-Layer Shortcut Reuse in DCNN Accelerators
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