Avoiding Data Inconsistency in Energy Harvesting Powered Embedded Systems

Energy harvesting is becoming a favorable alternative to power future generation embedded systems, as it is more environmentally and user friendly. However, energy harvesting powered embedded systems suffer from frequent execution interruption due to unstable energy supply. To tackle this problem, nonvolatile memory has been deployed to save the whole volatile state for computation. When power resumes, the processor can restore the state back to volatile memories and continue execution. However, without careful consideration, the process of checkpointing and resuming could cause inconsistency between volatile and nonvolatile memories, which leads to irreversible errors. In this article, we propose a consistency-aware adaptive checkpointing scheme that ensures correctness for all checkpoints. The proposed technique efficiently identifies all possible inconsistency positions in programs and inserts auxiliary code to ensure correctness by offline analysis. In addition, adaptive checkpointing assisted register file profiling and online tracking techniques further reduce the overhead of each checkpoint. Evaluation results show that the proposed checkpointing strategy can successfully eliminate inconsistency errors and greatly reduce the checkpointing overhead.