Forget Failure: Exploiting SRAM Data Remanence for Low-overhead Intermittent Computation

Energy harvesting is a promising solution to power billions of ultra-low-power Internet-of-Things devices to enable ubiquitous computing. However, energy harvesters typically output tiny amounts of energy and, therefore, cannot continuously power devices; this leads to intermittent computing, where the energy harvester periodically charges a capacitor to sufficient voltage to power brief computation, until the capacitor's charge is drained, and the cycle repeats. To retain program state across frequent power failures, prior work proposes checkpointing program state to Non-Volatile Memory (NVM) before a power failure. Unfortunately, the most widely deployed, highest performance, and lowest cost devices employ Flash as their NVM, but the power, time, and endurance limitations of Flash writes are incompatible with the frequent NVM checkpoints of intermittent computation.

The multi-year data retention of Flash is overkill for retaining program state across intermittent computing's short power-off times (e.g., <1s). We observe that even after computation stops due to low voltage, charge remains in the system; this remaining charge keeps the voltage high enough to maintain data in SRAM-effectively making it a NVM-for 10's of minutes post-power loss. This paper explores how to leverage SRAM's data remanence to boost common-case performance and energy efficiency for Flash-based intermittent computation systems. We propose TotalRecall, a library-level, in-situ, checkpointing technique that retains program state in SRAM and identifies when SRAM acts as a NVM, falling back to conventional NVM checkpoints in the rare event of long off times. Our evaluation, on real hardware, using benchmarks from Texas Instruments, shows that TotalRecall incurs overheads as low as 0.8%-up to over 350x faster than checkpointing to Flash.