# How to Manage Tiny Machine Learning at Scale: An Industrial Perspective

arXiv: https://arxiv.org/abs/2202.09113 · HTML: https://mlsanity.com/paper/2202.09113/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 35 references that are in the map.
- First posted on arXiv 18 Feb 2022.

## Builds on (most cited first, 10 of 10)

- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [TensorFlow Lite Micro: Embedded Machine Learning on TinyML Systems](https://mlsanity.com/paper/2010.08678/) (2020)
- [TinyOL: TinyML with Online-Learning on Microcontrollers](https://mlsanity.com/paper/2103.08295/) (2021)
- [TVM: An Automated End-to-End Optimizing Compiler for Deep Learning](https://mlsanity.com/paper/1802.04799/) (2018)
- [SOSA: A Lightweight Ontology for Sensors, Observations, Samples, and Actuators](https://mlsanity.com/paper/1805.09979/) (2018)
- [Widening Access to Applied Machine Learning with TinyML](https://mlsanity.com/paper/2106.04008/) (2021)
- [Fusion-DHL: WiFi, IMU, and Floorplan Fusion for Dense History of Locations in Indoor Environments](https://mlsanity.com/paper/2105.08837/) (2021)
- [The Synergy of Complex Event Processing and Tiny Machine Learning in Industrial IoT](https://mlsanity.com/paper/2105.03371/) (2021)
- [ML-EXray: Visibility into ML Deployment on the Edge](https://mlsanity.com/paper/2111.04779/) (2021)
- [Making Neural Networks FAIR](https://mlsanity.com/paper/1907.11569/) (2019)

## Built on by (most cited first, 5 of 5)

- [Enabling Resource-efficient AIoT System with Cross-level Optimization: A survey](https://mlsanity.com/paper/2309.15467/) (2023)
- [On-device Online Learning and Semantic Management of TinyML Systems](https://mlsanity.com/paper/2405.07601/) (2024)
- [TinyReptile: TinyML with Federated Meta-Learning](https://mlsanity.com/paper/2304.05201/) (2023)
- [SeLoC-ML: Semantic Low-Code Engineering for Machine Learning Applications in Industrial IoT](https://mlsanity.com/paper/2207.08818/) (2022)
- [Reinforcement Learning as a Parsimonious Alternative to Prediction Cascades: A Case Study on Image Segmentation](https://mlsanity.com/paper/2402.11760/) (2024)

## Abstract (arXiv)

Tiny machine learning (TinyML) has gained widespread popularity where machine learning (ML) is democratized on ubiquitous microcontrollers, processing sensor data everywhere in real-time. To manage TinyML in the industry, where mass deployment happens, we consider the hardware and software constraints, ranging from available onboard sensors and memory size to ML-model architectures and runtime platforms. However, Internet of Things (IoT) devices are typically tailored to specific tasks and are subject to heterogeneity and limited resources. Moreover, TinyML models have been developed with different structures and are often distributed without a clear understanding of their working principles, leading to a fragmented ecosystem. Considering these challenges, we propose a framework using Semantic Web technologies to enable the joint management of TinyML models and IoT devices at scale, from modeling information to discovering possible combinations and benchmarking, and eventually facilitate TinyML component exchange and reuse. We present an ontology (semantic schema) for neural network models aligned with the World Wide Web Consortium (W3C) Thing Description, which semantically describes IoT devices. Furthermore, a Knowledge Graph of 23 publicly available ML models and six IoT devices were used to demonstrate our concept in three case studies, and we shared the code and examples to enhance reproducibility: https://github.com/Haoyu-R/How-to-Manage-TinyML-at-Scale