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.

What problem does this paper address?

Tiny AI accelerators make fully on-wearable sensing and inference possible, but their energy behavior across hardware components, power modes, and end-to-end application stages was not well characterized. This makes it difficult to choose configurations that preserve battery life in continuous human-centered sensing.

Main contributions

• A component-level account of the MAX78000 hardware and its supported power configurations.
• Microbenchmarks of sensing, AI inference, general computation, memory I/O, and idle operation.
• Macrobenchmarks showing how system-wide configurations affect complete sensing-and-inference pipelines.
• Energy-optimization guidance for wearable, on-device sensing systems.

Key results

• The measurements show that application energy depends on both accelerator execution and surrounding operations such as sensing, memory I/O, computation, and idle configuration.

Relevant research topics

• Tiny AI Accelerators
• Microcontrollers
• Wearable Computing
• Human-Centered Sensing
• Energy Characterization
• On-Device Inference
• MAX78000

Preferred citation

@inproceedings{huang2024energy,
  author    = {Yushan Huang and Taesik Gong and SiYoung Jang and Fahim Kawsar and Chulhong Min},
  title     = {Energy Characterization of Tiny {AI} Accelerator-Equipped Microcontrollers},
  booktitle = {Proceedings of the 2nd International Workshop on Human-Centered Sensing, Networking, and Multi-Device Systems},
  year      = {2024},
  pages     = {1--6},
  doi       = {10.1145/3698388.3699628}
}