Nov. 1, 2022, 1:12 a.m. | Shvetank Prakash, Tim Callahan, Joseph Bushagour, Colby Banbury, Alan V. Green, Pete Warden, Tim Ansell, Vijay Janapa Reddi

cs.LG updates on arXiv.org arxiv.org

Need for the efficient processing of neural networks has given rise to the
development of hardware accelerators. The increased adoption of specialized
hardware has highlighted the need for more agile design flows for
hardware-software co-design and domain-specific optimizations. We present CFU
Playground, a full-stack open-source framework that enables rapid and iterative
design of machine learning (ML) accelerators for embedded ML systems. Our
toolchain integrates open-source software, open-source RTL generators, and
open-source FPGA tools for synthesis, place, and route. This full-stack …

arxiv framework full-stack machine machine learning playground stack tinyml

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