LATICS: A Low-overhead Adaptive Task-based Intermittent Computing System

Energy harvesting promises to power billions of Internet-of-Things devices without being restricted by battery life. The energy output of harvesters is typically tiny and highly unstable, so the computing system must store program states into nonvolatile memory frequently to preserve the execution progress in the presence of frequent power failures. Task-based intermittent computing is a promising paradigm to provide such capability, where each task executes atomically and only states across task boundaries need to be saved. This article presents LATICS, a low-overhead adaptive task-based intermittent computing system, which dynamically decides the granularity of atomic execution to avoid unnecessarily frequent state saving when energy supply is sufficient. The novel feature of LATICS is to drastically reduce the amount of states to be saved at task boundaries compared with existing solutions. Notably, we disclose that skipping state saving at some task boundary may cause the system to store more states at other places, and thus leads to higher overall overhead. Therefore, LATICS enforces mandatory state saving at certain task boundaries regardless of the current energy condition to reduce state saving overhead. We implement LATICS on a real energy-harvesting platform based on MSP430 and experimentally compare against the state-of-the-art under different settings. The experimental results show that LATICS significantly reduces state saving overhead and improves execution efficiency compared to existing solutions.