Deep Reinforcement-Learning-Guided Backup for Energy Harvesting Powered Systems

Energy harvesting technology has been widely developed as a promising alternative of battery to power embedded systems. However, energy harvesting powered embedded systems may have potential frequent power interruptions due to unstable energy supply. Nonvolatile processors (NVPs) are proposed to survive power failures by saving volatile data to nonvolatile memory (NVM) upon power failures and resuming them after power comes back. Traditionally, backup is triggered immediately when an energy warning occurs. However, it is also possible to more aggressively utilize the residual energy for program execution to improve forward progress. In this work, we propose a deep reinforcement-learning-guided backup strategy to improve forward progress in energy harvesting powered intermittent embedded systems. The experimental results show an average of 8.3%, 51.6%, and 325.3% improved forward progress compared with $Q$ -learning, the related work ALD, and traditional instant backup, respectively.