Deep neural network (DNN) inference in energy harvesting (EH) devices poses significant challenges due to resource constraints and frequent power interruptions. These power losses not only increase end-to-end latency, but also compromise inference consistency and accuracy, as existing checkpointing and restore mechanisms are prone to errors. Consequently, the quality of service (QoS) for DNN inference on EH devices is severely impacted. In this paper, we propose an energy-adaptive DNN inference mechanism capable of dynamically transitioning the model into a low-power mode by reducing computational complexity when harvested energy is limited. This approach ensures that end-to-end latency requirements are met. Additionally, to address the limitations of error-prone checkpoint-and-restore mechanisms, we introduce a checkpoint-free intermittent inference framework that ensures consistent, progress-preserving DNN inference during power failures in energy-harvesting systems.

翻译：能量收集（EH）设备上的深度神经网络（DNN）推理由于资源限制和频繁的电源中断而面临重大挑战。这些电源中断不仅增加了端到端延迟，还损害了推理的一致性和准确性，因为现有的检查点与恢复机制容易出错。因此，能量收集设备上DNN推理的服务质量（QoS）受到严重影响。本文提出了一种能量自适应的DNN推理机制，能够在收集能量有限时通过降低计算复杂度，动态地将模型切换到低功耗模式。该方法确保满足端到端延迟要求。此外，为了应对易出错的检查点与恢复机制的局限性，我们引入了一种免检查点的间歇推理框架，该框架确保在能量收集系统发生电源故障期间，实现一致且保持进度的DNN推理。