Implementing Spiking Neural Networks on Neuromorphic Architectures: A Review

TL;DR

This paper reviews the current state of software frameworks for implementing Spiking Neural Networks on neuromorphic hardware, emphasizing challenges in programming, performance, energy efficiency, and reliability.

Contribution

It provides a comprehensive overview of existing system software frameworks for neuromorphic SNNs, discussing design challenges and future opportunities.

Findings

Various frameworks support platform-based and hardware-software co-design.

Challenges include programming complexity, real-time performance, and energy efficiency.

Future opportunities involve improving system software for reliability and scalability.

Abstract

Recently, both industry and academia have proposed several different neuromorphic systems to execute machine learning applications that are designed using Spiking Neural Networks (SNNs). With the growing complexity on design and technology fronts, programming such systems to admit and execute a machine learning application is becoming increasingly challenging. Additionally, neuromorphic systems are required to guarantee real-time performance, consume lower energy, and provide tolerance to logic and memory failures. Consequently, there is a clear need for system software frameworks that can implement machine learning applications on current and emerging neuromorphic systems, and simultaneously address performance, energy, and reliability. Here, we provide a comprehensive overview of such frameworks proposed for both, platform-based design and hardware-software co-design. We highlight challenges and opportunities that the future holds in the area of system software technology for neuromorphic computing.