# Deploying Machine Learning Models to Ahead-of-Time Runtime on Edge Using MicroTVM

arXiv: https://arxiv.org/abs/2304.04842 · HTML: https://mlsanity.com/paper/2304.04842/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 18 references that are in the map.
- First posted on arXiv 10 Apr 2023.

## Builds on (most cited first, 4 of 4)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [TensorFlow Lite Micro: Embedded Machine Learning on TinyML Systems](https://mlsanity.com/paper/2010.08678/) (2020)
- [CMSIS-NN: Efficient Neural Network Kernels for Arm Cortex-M CPUs](https://mlsanity.com/paper/1801.06601/) (2018)
- [The SpiNNaker 2 Processing Element Architecture for Hybrid Digital Neuromorphic Computing](https://mlsanity.com/paper/2103.08392/) (2021)

## Built on by (most cited first, 3 of 3)

- [From Tiny Machine Learning to Tiny Deep Learning: A Survey](https://mlsanity.com/paper/2506.18927/) (2025)
- [NeuroDeX: Unlocking Diverse Support in Decompiling Deep Neural Network Executables](https://mlsanity.com/paper/2509.06402/) (2025)
- [The SpiNNaker2 chip: a many-core platform for flexible and scalable brain-inspired computing](https://mlsanity.com/paper/2607.24396/) (2026)

## Abstract (arXiv)

In the past few years, more and more AI applications have been applied to edge devices. However, models trained by data scientists with machine learning frameworks, such as PyTorch or TensorFlow, can not be seamlessly executed on edge. In this paper, we develop an end-to-end code generator parsing a pre-trained model to C source libraries for the backend using MicroTVM, a machine learning compiler framework extension addressing inference on bare metal devices. An analysis shows that specific compute-intensive operators can be easily offloaded to the dedicated accelerator with a Universal Modular Accelerator (UMA) interface, while others are processed in the CPU cores. By using the automatically generated ahead-of-time C runtime, we conduct a hand gesture recognition experiment on an ARM Cortex M4F core.