Machine learning (ML) is a vital part of many marketing tools. With security becoming more of an issues, many ML systems are migrating from the cloud to improve security and privacy.
However, there have been major start-up engineering costs for customized ML systems. Google, Purdue University and Harvard University focus on reducing this issue.
In the new paper CFU Playground: Full-Stack Open-Source Framework for Tiny Machine Learning (tinyML) Acceleration on FPGAs, a research team from Google, Purdue University and Harvard University introduces CFU Playground, a full-stack open-source framework that integrates open-source software, RTL (register-transfer level) generators, and FPGA (field-programmable gate array) tools to enable the rapid and iterative design of accelerators for embedded ML systems. Developers can use the framework to design custom function units (CFUs) for distinct ML operations.
In tests, even users with minimal FPGA or hardware experience were able to achieve model speedups of up to 75x.
Read more about Google, Perdue and Harvard U’s Open Source Framework.
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