Towards Machine Learning and Inference for Resource-constrained MCUs
Yushan Huang Hamed Haddadi
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.