MAFIA: Machine Learning Acceleration on FPGAs for IoT Applications
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 5 of its 43 references that are in the map. First posted on arXiv 8 Jul 2021.
Builds on
5 of 43 references.
References · 5
- 2016TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systemscited by 660
- 2018Fast inference of deep neural networks in FPGAs for particle physicscited by 132
- 2019FastGRNN: A Fast, Accurate, Stable and Tiny Kilobyte Sized Gated Recurrent Neural Networkcited by 38
- 2020Modeling Data Reuse in Deep Neural Networks by Taking Data-Types into Cognizancecited by 5
- 2020FastWave: Accelerating Autoregressive Convolutional Neural Networks on FPGAcited by 4
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Citing papers per year: 2022: 2.
2 papers.