This paper investigates the energy savings that near-subthreshold processors can obtain in edge AI applications and proposes strategies to improve them while maintaining the accuracy of the application. The selected processors deploy adaptive voltage scaling techniques in which the frequency and voltage levels of the processor core are determined at the run-time. In these systems, embedded RAM and flash memory size is typically limited to less than 1 megabyte to save power. This limited memory imposes restrictions on the complexity of the neural networks model that can be mapped to these devices and the required trade-offs between accuracy and battery life. To address these issues, we propose and evaluate alternative 'big-little' neural network strategies to improve battery life while maintaining prediction accuracy. The strategies are applied to a human activity recognition application selected as a demonstrator that shows that compared to the original network, the best configurations obtain an energy reduction measured at 80% while maintaining the original level of inference accuracy.

翻译：本文研究了近阈值处理器在边缘AI应用中可获取的能耗节省，并提出了在保持应用精度的同时改善能耗的策略。所选处理器采用自适应电压缩放技术，处理器核心的频率和电压等级在运行时确定。在这些系统中，为节省功耗，嵌入式RAM和闪存容量通常限制在1兆字节以下。有限的内存对可部署到这些设备的神经网络模型复杂度以及精度与电池寿命之间的必要权衡施加了限制。为解决这些问题，我们提出并评估了替代性的"大小"神经网络策略，以在保持预测精度的同时延长电池寿命。这些策略应用于选定作为演示器的人体活动识别应用，结果表明，与原始网络相比，最佳配置在保持原有推理精度水平的同时，实现了80%的能耗降低。