A Tiny Supervised ODL Core with Auto Data Pruning for Human Activity Recognition
This addresses the challenge of distributional shift in human activity recognition for edge devices, though it is incremental as it builds on existing ODL methods.
The paper tackles the problem of providing training labels for on-device learning in resource-limited edge devices by introducing a tiny supervised ODL core with automatic data pruning, reducing communication volume by 55.7% and power consumption to 3.39mW with only 0.9% accuracy loss.
In this paper, we introduce a low-cost and low-power tiny supervised on-device learning (ODL) core that can address the distributional shift of input data for human activity recognition. Although ODL for resource-limited edge devices has been studied recently, how exactly to provide the training labels to these devices at runtime remains an open-issue. To address this problem, we propose to combine an automatic data pruning with supervised ODL to reduce the number queries needed to acquire predicted labels from a nearby teacher device and thus save power consumption during model retraining. The data pruning threshold is automatically tuned, eliminating a manual threshold tuning. As a tinyML solution at a few mW for the human activity recognition, we design a supervised ODL core that supports our automatic data pruning using a 45nm CMOS process technology. We show that the required memory size for the core is smaller than the same-shaped multilayer perceptron (MLP) and the power consumption is only 3.39mW. Experiments using a human activity recognition dataset show that the proposed automatic data pruning reduces the communication volume by 55.7% and power consumption accordingly with only 0.9% accuracy loss.