Abstract

Machine learning (ML) is moving towards edge devices. However, ML models with high computational demands and energy consumption pose challenges for ML inference in resource-constrained environments, such as the deep sea. To address these challenges, we propose a battery-free ML inference and model personalization pipeline for microcontroller units (MCUs). As an example, we performed fish image recognition in the ocean. We evaluated and compared the accuracy, runtime, power, and energy consumption of the model before and after optimization. The results demonstrate that our pipeline can achieve 97.78% accuracy with 483.82 KB Flash, 70.32 KB RAM, 118 ms runtime, 4.83 mW power, and 0.57 mJ energy consumption on MCUs, reducing by 64.17%, 12.31%, 52.42%, 63.74%, and 82.67%, compared to the baseline. The results indicate the feasibility of battery-free ML inference on MCUs.

What problem does this paper address?

Remote environments such as the deep sea can make battery replacement or network-dependent inference impractical. This poster studies whether image recognition and personalization can fit within the memory, latency, power, and harvested-energy constraints of an MCU-based battery-free system.

Main contributions

• A battery-free MCU pipeline combining machine-learning inference with model personalization.
• A fish-image recognition case study for a remote ocean sensing scenario.
• Joint measurement of accuracy, flash and RAM use, runtime, power, and per-inference energy before and after optimization.

Key results

• The optimized MCU pipeline achieved 97.78% accuracy using 483.82 KB flash and 70.32 KB RAM.
• It ran in 118 ms at 4.83 mW and consumed 0.57 mJ per inference.
• Compared with the baseline, the reported reductions were 64.17% in flash, 12.31% in RAM, 52.42% in runtime, 63.74% in power, and 82.67% in energy.

Relevant research topics

• Battery-Free Computing
• TinyML
• Microcontrollers
• On-Device Inference
• Model Personalization
• Energy Harvesting
• Embedded Computer Vision

Preferred citation

@inproceedings{huang2023batteryfree,
  author    = {Yushan Huang and Hamed Haddadi},
  title     = {Poster: Towards Battery-Free Machine Learning Inference and Model Personalization on {MCUs}},
  booktitle = {Proceedings of the 21st Annual International Conference on Mobile Systems, Applications and Services},
  year      = {2023},
  pages     = {571--572},
  doi       = {10.1145/3581791.3597371}
}