Tactile hand gesture recognition is a crucial task for user control in the automotive sector, where Human-Machine Interactions (HMI) demand low latency and high energy efficiency. This study addresses the challenges of power-constrained edge training and inference by utilizing analog Resistive Random Access Memory (ReRAM) technology in conjunction with a real tactile hand gesture dataset. By optimizing the input space through a feature engineering strategy, we avoid relying on large-scale crossbar arrays, making the system more suitable for edge deployment. Through realistic hardware-aware simulations that account for device non-idealities derived from experimental data, we demonstrate the functionalities of our analog ReRAM-based analog in-memory computing for on-chip training, utilizing the state-of-the-art Tiki-Taka algorithm. Furthermore, we validate the classification accuracy of approximately 91.4% for post-deployment inference of hand gestures. The results highlight the potential of analog ReRAM technology and crossbar architecture with fully parallelized matrix computations for real-time HMI systems at the Edge.
@article{arxiv.2502.18152,
title = {Edge Training and Inference with Analog ReRAM Technology for Hand Gesture Recognition},
author = {Victoria Clerico and Anirvan Dutta and Donato Francesco Falcone and Wooseok Choi and Matteo Galetta and Tommaso Stecconi and András Horváth and Shokoofeh Varzandeh and Bert Jan Offrein and Mohsen Kaboli and Valeria Bragaglia},
journal= {arXiv preprint arXiv:2502.18152},
year = {2025}
}