Neuromorphic Circuits with Spiking Astrocytes for Increased Energy Efficiency, Fault Tolerance, and Memory Capacitance

TL;DR

This paper presents a neuromorphic circuit model incorporating spiking astrocytes to enhance energy efficiency, fault tolerance, and memory capacity in neural networks, demonstrating significant resilience improvements and robust memory management.

Contribution

Introduction of the Leaky Integrate-and-Fire Astrocyte (LIFA) model in SNNs, improving fault tolerance, energy efficiency, and memory management in neuromorphic systems.

Findings

Fault tolerance rate of 81.10% achieved

Resilience improvement rate of 18.90%

Enhanced memory management demonstrated

Abstract

In the rapidly advancing field of neuromorphic computing, integrating biologically-inspired models like the Leaky Integrate-and-Fire Astrocyte (LIFA) into spiking neural networks (SNNs) enhances system robustness and performance. This paper introduces the LIFA model in SNNs, addressing energy efficiency, memory management, routing mechanisms, and fault tolerance. Our core architecture consists of neurons, synapses, and astrocyte circuits, with each astrocyte supporting multiple neurons for self-repair. This clustered model improves fault tolerance and operational efficiency, especially under adverse conditions. We developed a routing methodology to map the LIFA model onto a fault-tolerant, many-core design, optimizing network functionality and efficiency. Our model features a fault tolerance rate of 81.10\% and a resilience improvement rate of 18.90\%, significantly surpassing other implementations. The results validate our approach in memory management, highlighting its potential as a robust solution for advanced neuromorphic computing applications. The integration of astrocytes represents a significant advancement, setting the stage for more resilient and adaptable neuromorphic systems.