🤖 AI Summary
This study addresses the challenge of deploying time-series models on resource-constrained microcontrollers, where computational and memory overheads are significant bottlenecks. Through a hardware-aware comparative analysis, the authors systematically evaluate the end-to-end deployment performance of LSTMs and 1D-CNNs across five time-series classification tasks. Their findings demonstrate, for the first time, that 1D-CNNs consistently outperform LSTMs in TinyML scenarios, achieving an average accuracy of 95%—approximately 6% higher than LSTMs—while simultaneously reducing RAM usage by 35% and Flash consumption by 25%. Moreover, inference latency is dramatically lowered from 2038 ms to 27.6 ms, representing a nearly 74-fold speedup. This work establishes 1D-CNNs as a high-accuracy, low-overhead paradigm for edge-based time-series modeling.
📝 Abstract
Time series classification underpins applications such as human activity recognition, healthcare monitoring, and gesture detection in the IoT domain. Tiny Machine Learning enables models to run directly on low-power microcontroller units, improving efficiency, ensuring privacy, and reducing cost by avoiding reliance on cloud or edge computing. While Long Short-Term Memory networks are widely used for capturing temporal dependencies, their high computational and memory demands make real-time MCU deployment impractical. In this work, we conduct a hardware-aware feasibility study of LSTM versus 1D Convolutional Neural Networks across five benchmark datasets. Results show that 1D-CNN consistently achieves comparable or higher accuracy around 95% than LSTM which is around 89%, while requiring 35% less RAM, approx. 25% less Flash, and enabling real-time inference that is 27.6 ms vs. 2038 ms. Being so lightweight, 1D-CNN is particularly suitable for on-device processing in wearables and other low-power, battery-operated systems, establishing it as a practical and resource-efficient choice for TinyML deployment.