Efficient Implementation of Spiking Neural Networks for Inference using Ex-Situ Training

My research paper „Efficient Implementation of Spiking Neural Networks for Inference using Ex-Situ Training“ was published in the IEEE Access (IF 3.4) journal today.

Abstract: This paper introduces a novel method for designing and simulating neuromorphic circuits for inference tasks, utilizing spiking neural networks (SNNs) trained ex-situ to offer practical advantages in efficiency and accuracy. The high effectiveness of this approach is demonstrated through efficient implementations for solving XOR and MNIST problems, achieved via specialized 2D crossbar circuit architectures integrated with operational amplifiers and Lapicque neuron models. Initially trained via a software application, the networks‘ weights are then transferred to neuromorphic circuit designs simulated with PySpice and Ngspice tools. Detailed simulations exhibit robust inference capabilities, achieving 100% accuracy on XOR problems and over 89% on MNIST datasets. Comprehensive listings of hardware components are provided for direct use in real-world applications. By validating the practicality of ex-situ trained SNNs in neuromorphic hardware, this work underscores their potential for future advanced computational tasks in neuromorphic systems and opens avenues for developing more efficient and powerful artificial intelligence (AI) systems.

The entire code implementation regarding this paper can be found on my GitHub.