Adaptive AI: Energy Efficient Multi-exit TinyML on Intelligent Vision Systems at the Edge
Traditional TinyML systems for edge devices achieve high accuracy by relying on fixed-depth models that require a constant number of multiply-accumulate (MAC) operations regardless of the input complexity. This approach wastes critical resources in battery-powered Internet-of-Things (IoT) devices and limits the real-time performance of edge cyber-physical systems. Multi-exit execution schemes mitigate these issues and are widely used on high-end devices such as GPUs, but are rarely exploited on edge IoT devices because they require substantial rethinking given their strict memory and computational constraints. We address these aspects by designing and deploying, on an ultra-low-power GWT ...
| Authors | Luca Crupi, Lorenzo Lamberti, Alessandro Giusti et al. |
|---|---|
| Primary category | cs.AR |
| Published | 7 July 2026 |
| Last updated at source | 7 July 2026 |
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