# Widening Access to Applied Machine Learning with TinyML

arXiv: https://arxiv.org/abs/2106.04008 · HTML: https://mlsanity.com/paper/2106.04008/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 124 references that are in the map.
- First posted on arXiv 7 Jun 2021.

## Builds on (most cited first, 19 of 19)

- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019)
- [How transferable are features in deep neural networks?](https://mlsanity.com/paper/1411.1792/) (2014)
- [Learning Transferable Architectures for Scalable Image Recognition](https://mlsanity.com/paper/1707.07012/) (2017)
- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition](https://mlsanity.com/paper/1804.03209/) (2018)
- [DeepDriving: Learning Affordance for Direct Perception in Autonomous Driving](https://mlsanity.com/paper/1505.00256/) (2015)
- [TensorFlow Lite Micro: Embedded Machine Learning on TinyML Systems](https://mlsanity.com/paper/2010.08678/) (2020)
- [TossingBot: Learning to Throw Arbitrary Objects with Residual Physics](https://mlsanity.com/paper/1903.11239/) (2019)
- [Hello Edge: Keyword Spotting on Microcontrollers](https://mlsanity.com/paper/1711.07128/) (2017)
- [Benchmarking TinyML Systems: Challenges and Direction](https://mlsanity.com/paper/2003.04821/) (2020)
- [DolphinAtack: Inaudible Voice Commands](https://mlsanity.com/paper/1708.09537/) (2017)
- [Temporal Convolution for Real-time Keyword Spotting on Mobile Devices](https://mlsanity.com/paper/1904.03814/) (2019)
- [Visual Wake Words Dataset](https://mlsanity.com/paper/1906.05721/) (2019)
- [Personalizing ASR for Dysarthric and Accented Speech with Limited Data](https://mlsanity.com/paper/1907.13511/) (2019)
- [TensorFlow.js: Machine Learning for the Web and Beyond](https://mlsanity.com/paper/1901.05350/) (2019)
- [A Cascade Architecture for Keyword Spotting on Mobile Devices](https://mlsanity.com/paper/1712.03603/) (2017)
- [See what I'm saying? Comparing Intelligent Personal Assistant use for Native and Non-Native Language Speakers](https://mlsanity.com/paper/2006.06328/) (2020)

## Built on by (most cited first, 17 of 17)

- [Edge Impulse: An MLOps Platform for Tiny Machine Learning](https://mlsanity.com/paper/2212.03332/) (2022)
- [TinyML: Tools, Applications, Challenges, and Future Research Directions](https://mlsanity.com/paper/2303.13569/) (2023)
- [Intelligence at the Extreme Edge: A Survey on Reformable TinyML](https://mlsanity.com/paper/2204.00827/) (2022)
- [Tiny, always-on and fragile: Bias propagation through design choices in on-device machine learning workflows](https://mlsanity.com/paper/2201.07677/) (2022)
- [How to Manage Tiny Machine Learning at Scale: An Industrial Perspective](https://mlsanity.com/paper/2202.09113/) (2022)
- [Machine Learning Sensors](https://mlsanity.com/paper/2206.03266/) (2022)
- [Is TinyML Sustainable? Assessing the Environmental Impacts of Machine Learning on Microcontrollers](https://mlsanity.com/paper/2301.11899/) (2023)
- [Software Engineering Approaches for TinyML based IoT Embedded Vision: A Systematic Literature Review](https://mlsanity.com/paper/2204.08702/) (2022)
- [Datasheets for Machine Learning Sensors](https://mlsanity.com/paper/2306.08848/) (2023)
- [Towards Contactless Elevators with TinyML using CNN-based Person Detection and Keyword Spotting](https://mlsanity.com/paper/2405.13051/) (2024)
- [Boosted Trees on a Diet: Compact Models for Resource-Constrained Devices](https://mlsanity.com/paper/2510.26557/) (2025)
- [TinyTorch: Building Machine Learning Systems from First Principles](https://mlsanity.com/paper/2601.19107/) (2026)
- [A review of TinyML](https://mlsanity.com/paper/2211.04448/) (2022)
- [Device management and network connectivity as missing elements in TinyML landscape](https://mlsanity.com/paper/2304.11669/) (2023)
- [SpokeN-100: A Cross-Lingual Benchmarking Dataset for The Classification of Spoken Numbers in Different Languages](https://mlsanity.com/paper/2403.09753/) (2024)
- [Integrating HCI Datasets in Project-Based Machine Learning Courses: A College-Level Review and Case Study](https://mlsanity.com/paper/2408.03472/) (2024)
- [Trainable Bitwise Soft Quantization for Input Feature Compression](https://mlsanity.com/paper/2603.05172/) (2026)

## Abstract (arXiv)

Broadening access to both computational and educational resources is critical to diffusing machine-learning (ML) innovation. However, today, most ML resources and experts are siloed in a few countries and organizations. In this paper, we describe our pedagogical approach to increasing access to applied ML through a massive open online course (MOOC) on Tiny Machine Learning (TinyML). We suggest that TinyML, ML on resource-constrained embedded devices, is an attractive means to widen access because TinyML both leverages low-cost and globally accessible hardware, and encourages the development of complete, self-contained applications, from data collection to deployment. To this end, a collaboration between academia (Harvard University) and industry (Google) produced a four-part MOOC that provides application-oriented instruction on how to develop solutions using TinyML. The series is openly available on the edX MOOC platform, has no prerequisites beyond basic programming, and is designed for learners from a global variety of backgrounds. It introduces pupils to real-world applications, ML algorithms, data-set engineering, and the ethical considerations of these technologies via hands-on programming and deployment of TinyML applications in both the cloud and their own microcontrollers. To facilitate continued learning, community building, and collaboration beyond the courses, we launched a standalone website, a forum, a chat, and an optional course-project competition. We also released the course materials publicly, hoping they will inspire the next generation of ML practitioners and educators and further broaden access to cutting-edge ML technologies.