Deep learning (DL) models have emerged as a promising solution for Internet
of Things (IoT) applications. However, due to their computational complexity,
DL models consume significant amounts of energy, which can rapidly drain the
battery and compromise the performance of IoT devices. For sustainable
operation, we consider an edge device with a rechargeable battery and energy
harvesting (EH) capabilities. In addition to the stochastic nature of the
ambient energy source, the harvesting rate is often insufficient to meet the
inference energy requirements, leading to drastic performance degradation in
energy-agnostic devices. To mitigate this problem, we propose energy-adaptive
dynamic early exiting (EE) to enable efficient and accurate inference in an EH
edge intelligence system. Our approach derives an energy-aware EE policy that
determines the optimal amount of computational processing on a per-sample
basis. The proposed policy balances the energy consumption to match the limited
incoming energy and achieves continuous availability. Numerical results show
that accuracy and service rate are improved up to 25% and 35%, respectively, in
comparison with an energy-agnostic policy.

提出了一种基于动态早停止和能量感知策略的边缘智能系统，用于解决深度学习模型在能源受限设备上的能耗问题，实现高效、精准的推理，与传统的能量不感知策略相比，精度和服务率分别提高了至多 25% 和 35%。