1. e-G2C: A 0.14-to-8.31 $\mu$J/Inference NN-based Processor with Continuous On-chip Adaptation for Anomaly Detection and ECG Conversion from EGM
- Author
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Zhao, Yang, Zhang, Yongan, Fu, Yonggan, Ouyang, Xu, Wan, Cheng, Wu, Shang, Banta, Anton, John, Mathews M., Post, Allison, Razavi, Mehdi, Cavallaro, Joseph, Aazhang, Behnaam, Lin, Yingyan, Zhao, Yang, Zhang, Yongan, Fu, Yonggan, Ouyang, Xu, Wan, Cheng, Wu, Shang, Banta, Anton, John, Mathews M., Post, Allison, Razavi, Mehdi, Cavallaro, Joseph, Aazhang, Behnaam, and Lin, Yingyan
- Abstract
This work presents the first silicon-validated dedicated EGM-to-ECG (G2C) processor, dubbed e-G2C, featuring continuous lightweight anomaly detection, event-driven coarse/precise conversion, and on-chip adaptation. e-G2C utilizes neural network (NN) based G2C conversion and integrates 1) an architecture supporting anomaly detection and coarse/precise conversion via time multiplexing to balance the effectiveness and power, 2) an algorithm-hardware co-designed vector-wise sparsity resulting in a 1.6-1.7$\times$ speedup, 3) hybrid dataflows for enhancing near 100% utilization for normal/depth-wise(DW)/point-wise(PW) convolutions (Convs), and 4) an on-chip detection threshold adaptation engine for continuous effectiveness. The achieved 0.14-8.31 $\mu$J/inference energy efficiency outperforms prior arts under similar complexity, promising real-time detection/conversion and possibly life-critical interventions, Comment: Accepted by 2022 IEEE Symposium on VLSI Technology and Circuits (VLSI Technology and Circuits)
- Published
- 2022
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