CFU Playground: Want a faster ML processor? Do it yourself!
DATE(2023)
摘要
The rise of machine learning (ML) has necessitated the development of innovative processing engines. However, development of specialized hardware accelerators can incur enormous one-time engineering expenses that should be avoided in low-cost embedded ML systems. In addition, embedded systems have tight resource constraints that prevent them from affording the "full-blown" machine learning (ML) accelerators seen in many cloud environments. In embedded situations, a custom function unit (CFU) that is more lightweight is preferable. We offer CFU Playground, an open-source toolchain for accelerating embedded machine learning (ML) on FPGAs through the use of CFUs.
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关键词
CFU Playground,devel-opment,embedded machine learning,embedded situations,embedded systems,faster ML processor,full-blown machine learning accelerators,innovative processing engines,low-cost embedded ML systems,one-time engineering expenses,specialized hardware accelerators,tight resource constraints
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