Advances in Neuromorphic Spin-Based Spiking Neural Networks: A review

This article reviews the recent developments and challenges in spintronic based spiking neural networks (SNNs). The present CPUs and GPUs are powerful tools that are capable of computing complex artificial intelligence (AI) tasks, yet they face several challenges. The systems utilizing machine learning techniques require extensive computations including vector-matrix multiplication. The traditional von Neumann architecture is inefficient in the hardware implementation of such systems. Neuromorphic architectures are computationally more efficient that ushers the era of high-performance hardware for AI applications. This also caters to the need of hardware solutions for biologically inspired in-memory computing functionality using nonvolatile memory (NVM). These biologically inspired networks have attracted a great attention due to their inherent computational efficiency in performing pattern/speech recognition and image classification tasks. During the last decade, SNNs have emerged as one of the suitable solutions for imitating the biological brain. Information is temporarily encoded in SNNs and the spikes serve as a mode of communication between the neurons. Various NVM devices such as spin-based memories, resistive randomaccess memory (RRAM), phase change memory (PCM), and conductive-bridging RAM (CBRAM) have been explored for the hardware implementation of NNs. Spin-based memory devices demonstrate much more energy efficiency while providing superior information processing capabilities than other NVMs. These devices also provide high endurance, scalability, CMOS compatibility, and ultralow power consumption characteristics. The spiking functionality in neurons can be easily mapped to various spin devices with synaptic learning, therefore, paving the path for future AI hardware solutions.