Bayesian Deep-Learning Processor for Real-Time Bio-Applications With Structured Monte Carlo Dropout for High-Volume Sample Generation

초록

This work presents a Bayesian neural network (BNN) processor for real-time, edge-based medical applications, designed to generate high-volume inference outputs efficiently, providing rapid and precise uncertainty estimation. To address the computational and memory-intensive demands of BNNs, we propose a structured Monte Carlo (MC) dropout method and an efficient processing technique for depth-wise separable convolution, thereby minimizing both the memory and computational burdens. The proposed processor, implemented in 28-nm LP CMOS, is benchmarked on a 12-lead ECG dataset and demonstrates significant improvements in energy efficiency, achieving a 12.9 & times; enhancement over conventional MC dropout methods. Furthermore, the processor incorporates a model-switching technique based on uncertainty estimation, resulting in 3.2 & times; lower energy consumption while maintaining robust and accurate ECG classification, even under challenging conditions involving noise and motion artifacts.

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