Energy-harvesting devices are the key to enabling future ubiquitous sensing applications, because they are long lived and require little maintenance. On-device processing of sensed data, such as images, avoids the high energy cost of communicating data to the edge or cloud. This letter observes that the on-device computing performance of an energy-harvesting system depends not only on execution time, but also on energy collection time. With high input power, a faster, higher-power processor quickly completes processing because energy collection time is low. At low input power, a slower, more energy efficient processor minimizes end-to-end latency by more judiciously using the slowly collected energy. This letter describes the PHASE model, which captures this charge latency effect. Using the model, we develop PHASE architectures, which include heterogeneous processing components of different efficiency and performance. A PHASE architecture uses the combination of heterogeneous components that minimizes end-to-end latency, including recharge time. Our results show that the PHASE model helps understand end-to-end latency in an energy harvesting device, yielding PHASE architectures that complete up to $9\times$9× more work on a fixed energy budget than typical energy-harvesting architecture.

中文翻译：

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能量收集计算机的功耗感知异构架构扩展

能量收集设备是实现未来无处不在的传感应用的关键，因为它们寿命长且几乎不需要维护。感测数据（例如图像）的设备上处理避免了将数据传输到边缘或云的高能源成本。这封信观察到能量收集系统的设备上计算性能不仅取决于执行时间，还取决于能量收集时间。在高输入功率下，更快、更高功率的处理器可以快速完成处理，因为能量收集时间很短。在低输入功率下，更慢、更节能的处理器通过更明智地使用缓慢收集的能量来最大限度地减少端到端延迟。这封信描述了捕获这种电荷延迟效应的 PHASE 模型。使用该模型，我们开发了 PHASE 架构，其中包括不同效率和性能的异构处理组件。PHASE 架构使用异构组件的组合，最大限度地减少端到端延迟，包括充电时间。我们的结果表明，PHASE 模型有助于理解能量收集设备中的端到端延迟，从而产生最多可完成的 PHASE 架构$9\times$9× 与典型的能量收集架构相比，在固定能量预算上的工作更多。