论文标题
E-G2C：0.14至8.31 $μ$ j/temperion nn的处理器，具有连续的芯片适应以进行异常检测和EGM的ECG转换
e-G2C: A 0.14-to-8.31 $μ$J/Inference NN-based Processor with Continuous On-chip Adaptation for Anomaly Detection and ECG Conversion from EGM
论文作者
论文摘要
这项工作介绍了第一个硅验证的专用EGM至ECG（G2C）处理器，称为E-G2C，具有连续的轻质异常检测，事件驱动的粗/精确转换和芯片适应。 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正常/深度（DW）/点（PW）卷积（Convs）和4）片上检测阈值适应引擎，以实现连续有效性。实现的0.14-8.31 $μ$ J/推理能源效率在相似的复杂性下优于先前的艺术，有望实时检测/转换以及可能的关键生活干预措施
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 $μ$J/inference energy efficiency outperforms prior arts under similar complexity, promising real-time detection/conversion and possibly life-critical interventions
