# MAFIA: Machine Learning Acceleration on FPGAs for IoT Applications

arXiv: https://arxiv.org/abs/2107.03653 · HTML: https://mlsanity.com/paper/2107.03653/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 43 references that are in the map.
- First posted on arXiv 8 Jul 2021.

## Builds on (most cited first, 5 of 5)

- [TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems](https://mlsanity.com/paper/1603.04467/) (2016)
- [Fast inference of deep neural networks in FPGAs for particle physics](https://mlsanity.com/paper/1804.06913/) (2018)
- [FastGRNN: A Fast, Accurate, Stable and Tiny Kilobyte Sized Gated Recurrent Neural Network](https://mlsanity.com/paper/1901.02358/) (2019)
- [Modeling Data Reuse in Deep Neural Networks by Taking Data-Types into Cognizance](https://mlsanity.com/paper/2008.02565/) (2020)
- [FastWave: Accelerating Autoregressive Convolutional Neural Networks on FPGA](https://mlsanity.com/paper/2002.04971/) (2020)

## Built on by (most cited first, 2 of 2)

- [T-RECX: Tiny-Resource Efficient Convolutional neural networks with early-eXit](https://mlsanity.com/paper/2207.06613/) (2022)
- [MinUn: Accurate ML Inference on Microcontrollers](https://mlsanity.com/paper/2210.16556/) (2022)

## 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.