Machine Learning Meets Internet of Things: From Theory to Practice

Overview

Standalone execution of problem-solving Artificial Intelligence (AI) on IoT devices produces a higher level of autonomy and privacy. This is because the sensitive user data collected by the devices need not be transmitted to the cloud for inference. The chipsets used to design IoT devices are resource-constrained due to their limited memory footprint, fewer computation cores, and low clock speeds. These limitations constrain one from deploying and executing complex problem-solving AI (usually an ML model) on IoT devices. Since there is a high potential for building intelligent IoT devices, in this tutorial, we teach researchers and developers; (i) How to deep compress CNNs and efficiently deploy on resource-constrained devices; (ii) How to efficiently port and execute ranking, regression, and classification problems solving ML classifiers on IoT devices; (iii) How to create ML-based self-learning devices that can locally re-train themselves on-the-fly using the unseen real-world data.

Tutorial webpage: https://sites.google.com/view/ml-meets-iot-ecml-tutorial/

Aims and Learning Objectives

Through this tutorial, we aim to interconnect the Software Engineering, Internet of Things, Machine Learning communities by bringing together the technology from each community in order to develop AI-enabled, self-learning, and offline inference performing autonomous IoT devices/products. The learning objectives of the tutorial are the following:

1. For beginners, it will create an end-to-end understanding of how to optimize a given problem-solving ML model and deploy it on resource-constrained devices for offline analytics.

2. Practitioners can improve the inference performance and compression levels of their use-case ML models, which they plan to deploy on their commercial IoT devices/products.

3. Researchers, when benchmarking a ML model by executing it on real-world devices using the tutorial Part IV technique, can obtain superior experimental results in their papers.

4. For ML experts, it will express the need of designing resource-friendly models in order to speed up the R & D phase (going from idea to product) of ML-powered IoT devices.

Tutorial Material

We will deliver the concepts using Powerpoint slides embedded with animations and small code snippets. During content delivery, the audience will be asked to perform small and quick exercises to make the tutorial interactive. We also will interleave live/recorded demonstrations throughout the tutorial to improve the audience's understanding and also to provide them opportunities to study technology in action. The tutorial will have four parts (content provided in folders named Part I to Part IV) with hands-on exercises relevant to each session.

Required Prior Knowledge

Since this tutorial makes use of concepts from both ML and IoT, the ideal preparation would be the basics of Arduino IDE, MCU boards, and basic ML models. Although our step-by-step tutorial will guide the audience on how to implement the tutorial covered technologies, the knowledge of programming languages such as Python, C/C++, and the set up of the Google Colab/Jupyter notebook would be beneficial for the hands-on and demo session.

If the tutorial is useful, please consider staring this GitHub repository and citing it using the BibTex entry below.

@article{mlmeetsiot2021,
  title = {Machine learning meets internet of things: From theory to practice},
  booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)},
  author = {Sudharsan, Bharath and Patel, Pankesh},
  url = {https://scholarcommons.sc.edu/aii_fac_pub/508/},
  year = {2021}