# TinyML for Ubiquitous Edge AI

arXiv: https://arxiv.org/abs/2102.01255 · HTML: https://mlsanity.com/paper/2102.01255/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 16 references that are in the map.
- First posted on arXiv 2 Feb 2021.

## Builds on (most cited first, 8 of 8)

- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Learning both Weights and Connections for Efficient Neural Networks](https://mlsanity.com/paper/1506.02626/) (2015)
- [ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices](https://mlsanity.com/paper/1707.01083/) (2017)
- [Network Trimming: A Data-Driven Neuron Pruning Approach towards Efficient Deep Architectures](https://mlsanity.com/paper/1607.03250/) (2016)
- [Benchmarking TinyML Systems: Challenges and Direction](https://mlsanity.com/paper/2003.04821/) (2020)
- [Benchmark Analysis of Representative Deep Neural Network Architectures](https://mlsanity.com/paper/1810.00736/) (2018)
- [Light Gated Recurrent Units for Speech Recognition](https://mlsanity.com/paper/1803.10225/) (2018)
- [Convolutional-Recurrent Neural Networks on Low-Power Wearable Platforms for Cardiac Arrhythmia Detection](https://mlsanity.com/paper/2001.03538/) (2020)

## Built on by (most cited first, 13 of 13)

- [Intelligence at the Extreme Edge: A Survey on Reformable TinyML](https://mlsanity.com/paper/2204.00827/) (2022)
- [Quantized Transformer Language Model Implementations on Edge Devices](https://mlsanity.com/paper/2310.03971/) (2023)
- [TinyML Platforms Benchmarking](https://mlsanity.com/paper/2112.01319/) (2021)
- [From Tiny Machine Learning to Tiny Deep Learning: A Survey](https://mlsanity.com/paper/2506.18927/) (2025)
- [Software Engineering Approaches for TinyML based IoT Embedded Vision: A Systematic Literature Review](https://mlsanity.com/paper/2204.08702/) (2022)
- [MEMA Runtime Framework: Minimizing External Memory Accesses for TinyML on Microcontrollers](https://mlsanity.com/paper/2304.05544/) (2023)
- [Edge Artificial Intelligence: A Systematic Review of Evolution, Taxonomic Frameworks, and Future Horizons](https://mlsanity.com/paper/2510.01439/) (2025)
- [A review of TinyML](https://mlsanity.com/paper/2211.04448/) (2022)
- [Distributed Agent-Based Collaborative Learning in Cross-Individual Wearable Sensor-Based Human Activity Recognition](https://mlsanity.com/paper/2311.04236/) (2023)
- [MosquIoT: A System Based on IoT and Machine Learning for the Monitoring of Aedes aegypti (Diptera: Culicidae)](https://mlsanity.com/paper/2401.16258/) (2024)
- [DiTMoS: Delving into Diverse Tiny-Model Selection on Microcontrollers](https://mlsanity.com/paper/2403.09035/) (2024)
- [Integration of TinyML and LargeML: A Survey of 6G and Beyond](https://mlsanity.com/paper/2505.15854/) (2025)
- [Power Side-Channel Membership Inference Attack on Embedded Machine Learning](https://mlsanity.com/paper/2610.10909/) (2026)

## Abstract (arXiv)

TinyML is a fast-growing multidisciplinary field at the intersection of machine learning, hardware, and software, that focuses on enabling deep learning algorithms on embedded (microcontroller powered) devices operating at extremely low power range (mW range and below). TinyML addresses the challenges in designing power-efficient, compact deep neural network models, supporting software framework, and embedded hardware that will enable a wide range of customized, ubiquitous inference applications on battery-operated, resource-constrained devices. In this report, we discuss the major challenges and technological enablers that direct this field's expansion. TinyML will open the door to the new types of edge services and applications that do not rely on cloud processing but thrive on distributed edge inference and autonomous reasoning.