On the Sweet Spot of Intermittent Inference in the Battery-less Internet of Things
Year: 2025
Type: conference-paper
Abstract: 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.
Authors: Rei Barjami, Antonio Miele, Luca Mottola
Institution: Politecnico di Milano
Language: English
Subfield: Hardware and Architecture
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7129050647