  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
