Recent breakthroughs in ML have produced new classes of models that allow ML inference to run directly on milliwatt-powered IoT devices. On one hand, existing ML-to-FPGA compilers are designed for deep neural-networks on large FPGAs. On the other hand, general-purpose HLS tools fail to exploit properties specific to ML inference, thereby resulting in suboptimal performance. We propose MAFIA, a tool to compile ML inference on small form-factor FPGAs for IoT applications. MAFIA provides native support for linear algebra operations and can express a variety of ML algorithms, including state-of-the-art models. We show that MAFIA-generated programs outperform best-performing variant of a commercial HLS compiler by 2.5x on average.
@article{arxiv.2107.03653,
title = {MAFIA: Machine Learning Acceleration on FPGAs for IoT Applications},
author = {Nikhil Pratap Ghanathe and Vivek Seshadri and Rahul Sharma and Steve Wilton and Aayan Kumar},
journal= {arXiv preprint arXiv:2107.03653},
year = {2021}
}
Comments
Accepted at The International Conference on Field-Programmable Logic and Applications (FPL), 2021