@inproceedings{ghanathe2021mafia,
author = {Ghanathe, Nikhil Pratap and Seshadri, Vivek and Sharma, Rahul and Wilton, Steve and Kumar, Aayan},
title = {MAFIA: Machine Learning Acceleration on FPGAs for IoT Applications},
booktitle = {FPL},
year = {2021},
month = {September},
abstract = {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.},
publisher = {IEEE},
url = {http://approjects.co.za/?big=en-us/research/publication/mafia-machine-learning-acceleration-on-fpgas-for-iot-applications/},
}