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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. [ABSTRACT FROM AUTHOR]

Since static random access memory (SRAM)-based field-programmable gate array (FPGA) has limited density and comparatively high leakage power, researchers have proposed FPGA architectures based on emerging nonvolatile memories (NVMs) to satisfy the requirements of data-intensive and low-power applications. Among all components, block random access memory (BRAM) has the severest endurance problem in FPGA. Unluckily, traditional wear leveling (TWL) strategies cannot be directly applied to nonvolatile FPGA because it may induce large performance overhead. In this article, we propose performance-aware wear leveling schemes for nonvolatile FPGA to improve its lifetime. Two strategies pertaining to coarse-grained wear leveling (C-Pearl) and fine-grained wear leveling (F-Pearl) are developed to balance inter-BRAM and intra-BRAM writes. Procedures, including static analysis, wear leveling-guided placement, and reconfiguration are discussed. A supportive circuit design is proposed, too. The evaluation shows that C-Pearl and F-Pearl can achieve 34% and 46% higher lifetime improvement and simultaneously 8% and 11% lower performance overhead than TWL. [ABSTRACT FROM AUTHOR]