Unlocking the Path Towards Automation of Tiny Machine Learning for Edge Computing

ThirstyAI and resource-constrained environments have become evergrowing challenges for Machine Learning (ML), especially large-scale Deep Learning (DL) models. This paper introduces NanoDeploy Automator, an Automated Machine Learning (AutoML) subsystem for the TANDEM native Artificial Intelligence (AI) edge platform, designed for efficient and reliable Tiny Machine Learning (TinyML), addressing the need for smaller models with minimal loss of accuracy and automated model compression and deployment at the edge. This work has been conducted in scope of project TANDEM and is currently expanded and adapted, forming the basis for the TinyML mechanisms of project SUNRISE-6G. The paper showcases the novel application of Time Generative Pre-Trained Transformer (TimeGPT) in real-world Internet of Things (IoT) scenarios, emphasizing three key contributions: a cutting-edge Automated TinyML (AutoTinyML) pipeline, the integration of TimeGPT in IoT, and a comprehensive evaluation of time series forecasting and anomaly detection techniques. Evaluation results demonstrate the efficiency of the AutoTinyML pipeline in terms of accuracy and model compression. The proposed system holds promise for diverse applications, leveraging the power of AutoTinyML, and reflects a significant stride towards democratizing access to accurate predictions and reducing uncertainty in edge computing scenarios, eliminating the need for a dedicated team of ML engineers.