Tiny Machine Learning for Concept Drift

• Published in: IEEE transaction on neural networks and learning systems 2024-06, Vol.35 (6), p.8470-8481
• Main Authors: Disabato, Simone, Roveri, Manuel
• Format: Article
• Language: English
• Subjects: Actuators; Adaptation; Benchmarks; Change detection; Computer applications; Computer memory; concept drift; Concept learning; Deep learning; deep learning (DL); Drift; Edge computing; Embedded systems; Feature extraction; Internet of Things; k-nearest neighbor (k-NN); Knowledge bases (artificial intelligence); Learning algorithms; Learning systems; Machine learning; Memory management; Modules; Obsolescence; Periodicity; Seasonal variations; tiny machine learning (TML); Training
• ISSN: 2162-237X; 2162-2388
• DOI: 10.1109/TNNLS.2022.3229897

Tiny machine learning (TML) is a new research area whose goal is to design machine and deep learning (DL) techniques able to operate in embedded systems and the Internet-of-Things (IoT) units, hence satisfying the severe technological constraints on memory, computation, and energy characterizing these pervasive devices. Interestingly, the related literature mainly focused on reducing the computational and memory demand of the inference phase of machine and deep learning models. At the same time, the training is typically assumed to be carried out in cloud or edge computing systems (due to the larger memory and computational requirements). This assumption results in TML solutions that might become obsolete when the process generating the data is affected by concept drift (e.g., due to periodicity or seasonality effect, faults or malfunctioning affecting sensors or actuators, or changes in the users' behavior), a common situation in real-world application scenarios. For the first time in the literature, this article introduces a TML for concept drift (TML-CD) solution based on deep learning feature extractors and a k -nearest neighbors ( k -NNs) classifier integrating a hybrid adaptation module able to deal with concept drift affecting the data-generating process. This adaptation module continuously updates (in a passive way) the knowledge base of TML-CD and, at the same time, employs a change detection test (CDT) to inspect for changes (in an active way) to quickly adapt to concept drift by removing obsolete knowledge. Experimental results on both image and audio benchmarks show the effectiveness of the proposed solution, whilst the porting of TML-CD on three off-the-shelf micro-controller units (MCUs) shows the feasibility of what is proposed in real-world pervasive systems.