On the Sweet Spot of Intermittent Inference in the Battery-less Internet of Things

We present a measurement and performance analysis of system-level settings to improve the energy efficiency of Deep Neural Network (DNN) inference on battery-less Internet of Things (IoT) devices. To do so, we deliberately trade a small, controllable reduction in inference accuracy for energy gains. Battery-less IoT devices are severely resource-constrained platforms powered by energy harvesting, where execution becomes intermittent as it alternates between bursts of computation and periods of energy recharge. To survive frequent energy failures, devices persist their system state into non-volatile memories, incurring significant energy costs. We leverage aggressive current scaling offered by Spin-Transfer Torque Magnetic RandomAccess Memory (STT-MRAM) during state writes to reduce energy consumption, intentionally allowing controlled write errors that affect inference outcomes. Through an extensive experimental campaign comprising over 2.2+ trillion data points across 4 microcontroller units (MCUs) and 8 benchmarks, we demonstrate that by tolerating a limited accuracy loss we can obtain up to 40% energy savings. We release our framework and toolset to foster further research in this emerging design space.