能量收集动力设备的非均匀压缩多出口神经网络间歇推理
Intermittent Inference with Nonuniformly Compressed Multi-Exit Neural Network for Energy Harvesting Powered Devices
摘要：
这项工作的目的是通过将轻量级DNN部署到能量收集（EH）供电的设备上，为EH供电的设备实现持久的、事件驱动的感知和决策能力。然而，收集的能量通常很弱且不可预测，即使是轻量级DNN也需要多个电源周期才能完成一个推理。为了消除为一个推理积累能量的无限期长时间等待并优化准确性，我们开发了一种功率跟踪感知和出口引导的网络压缩算法，以压缩和部署多出口神经网络到EH供电的微控制器（MCU），并在执行期间根据可用能量选择出口。实验结果表明，优越的准确性和延迟相比，国家的最先进的技术。
英文摘要：
This work aims to enable persistent, event-driven sensing and decision capabilities for energy-harvesting (EH)-powered devices by deploying lightweight DNNs onto EH-powered devices. However, harvested energy is usually weak and unpredictable and even lightweight DNNs take multiple power cycles to finish one inference. To eliminate the indefinite long wait to accumulate energy for one inference and to optimize the accuracy, we developed a power trace-aware and exit-guided network compression algorithm to compress and deploy multi-exit neural networks to EH-powered microcontrollers (MCUs) and select exits during execution according to available energy. The experimental results show superior accuracy and latency compared with state-of-the-art techniques.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
