Energy Characterization of Tiny AI Accelerator-Equipped Microcontrollers

Abstract

Tiny AI accelerators are seamlessly integrated into wearable devices due to their small form factor, enabling human sensing applications to run solely on wearables. However, despite this potential, the energy characterization of these tiny AI accelerators has been hardly studied, which is a key enabler for realizing such applications in our daily lives. In this paper, we present a comprehensive analysis of the energy characterization of ultra-low power microcontrollers using MAX78000 manufactured by Analog Device. We detailed the hardware components and their supported power configurations. We then conducted extensive benchmarks at micro and macro levels. For micro-level benchmarks, we evaluated the power/energy consumption under individual system configuration involved in each operation–sensing, AI inference, computation, memory I/O, and idle. For macro-level benchmarks, we analyzed the impact of system-wide configurations on overall energy consumption of end-to-end application pipelines. Our findings offer valuable insights into energy optimization for wearable systems with on-device and human-centered sensing technologies.