Data Backup Optimization for Nonvolatile SRAM in Energy Harvesting Sensor Nodes

Nonvolatile static random access memory (nvSRAM) has been widely investigated as a promising on-chip memory architecture in energy harvesting sensor nodes, due to zero standby power, resilience to power failures, and fast read/write operations. However, conventional approaches back up all data from static random access memory into nonvolatile memory when power failures happen. It leads to significant energy overhead and peak inrush current, which has a negative impact on the system performance and circuit reliability. This paper proposes a holistic data backup optimization to mitigate these problems in nvSRAM, consisting of a partial backup algorithm and a run-time adaptive write policy. A statistic dead-block predictor is employed to achieve dead block identification with trivial hardware overhead. An adaptive policy is used to switch between write-back and write-through strategy to reduce the rollback induced by backup failures. Experimental results show that the proposed scheme improves the performance by 4.6% on average while the backup power consumption and the inrush current are reduced by 38.1% and 54% on average compared to the full backup scheme. What is more, the backup capacitor size for energy buffer can be reduced by 40% on average under the same performance constraint.

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