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MSPAN: A Memristive Spike-Based Computing Engine With Adaptive Neuron for Edge Arrhythmia Detection.

Jingwen Jiang1, Fengshi Tian1, Jinhao Liang1

  • 1State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai, China.

Frontiers in Neuroscience
|January 3, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces an energy-efficient memristive spike-based computing in memory system for accurate remote arrhythmia detection on edge devices. The MSPAN system achieves high accuracy with low power consumption, making it ideal for real-time health monitoring.

Keywords:
arrhythmia detectioncomputation in memorymemristiveneuromorphic computingspike-based

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Area of Science:

  • Neuro-inspired computing
  • Edge AI
  • Biomedical engineering

Background:

  • Arrhythmia detection is crucial for cardiovascular health monitoring.
  • Existing edge devices face challenges in energy efficiency and accuracy for complex tasks like arrhythmia detection.
  • Spiking neural networks (SNNs) offer potential for low-power, efficient computation.

Purpose of the Study:

  • To propose a memristive spike-based computing in memory (CIM) system with adaptive neuron (MSPAN) for energy-efficient and accurate remote arrhythmia detection.
  • To co-design software and hardware for optimizing performance on edge devices.
  • To demonstrate the system's effectiveness using electrocardiogram (ECG) data.

Main Methods:

  • Designed a multi-layer deep integrative spiking neural network (DiSNN) for 4-class ECG classification.
  • Developed a memristor-based CIM architecture and a mapping method for deploying the DiSNN.
  • Integrated adaptive neurons into the system for enhanced performance.

Main Results:

  • The DiSNN achieved 93.6% accuracy in 4-class ECG classification.
  • The overall MSPAN system demonstrated over 92.25% accuracy on the MIT-BIH dataset.
  • The system operated with a small area (3.438 mm²) and low power consumption (0.178 μJ per heartbeat) at 500 MHz.

Conclusions:

  • The proposed MSPAN system offers a promising solution for high-accuracy, energy-efficient arrhythmia detection in edge devices.
  • Software and hardware co-design is effective for deploying complex AI models on resource-constrained edge platforms.
  • Memristive CIM technology is well-suited for real-time biomedical signal processing applications.