School of Engineering, Deakin University, Geelong, VIC 3216, Australia
School of Information Technology, Deakin University (Burwood Campus) Burwood, VIC 3125, Australia
Neural Comput. 2022 May 19;34(6):1289-1328. doi: 10.1162/neco_a_01499.
Artificial neural networks (ANNs) have experienced a rapid advancement for their success in various application domains, including autonomous driving and drone vision. Researchers have been improving the performance efficiency and computational requirement of ANNs inspired by the mechanisms of the biological brain. Spiking neural networks (SNNs) provide a power-efficient and brain-inspired computing paradigm for machine learning applications. However, evaluating large-scale SNNs on classical von Neumann architectures (central processing units/graphics processing units) demands a high amount of power and time. Therefore, hardware designers have developed neuromorphic platforms to execute SNNs in and approach that combines fast processing and low power consumption. Recently, field-programmable gate arrays (FPGAs) have been considered promising candidates for implementing neuromorphic solutions due to their varied advantages, such as higher flexibility, shorter design, and excellent stability. This review aims to describe recent advances in SNNs and the neuromorphic hardware platforms (digital, analog, hybrid, and FPGA based) suitable for their implementation. We present that biological background of SNN learning, such as neuron models and information encoding techniques, followed by a categorization of SNN training. In addition, we describe state-of-the-art SNN simulators. Furthermore, we review and present FPGA-based hardware implementation of SNNs. Finally, we discuss some future directions for research in this field.
人工神经网络（ANNs）在自主驾驶和无人机视觉等各种应用领域取得了成功，因此得到了快速发展。研究人员受到生物大脑机制的启发，一直在提高 ANN 的性能效率和计算要求。尖峰神经网络（SNNs）为机器学习应用提供了一种高能效和受大脑启发的计算范例。然而，在经典冯·诺依曼架构（中央处理器/图形处理器）上评估大规模 SNN 需要大量的电力和时间。因此，硬件设计人员已经开发出神经形态平台来执行 SNN，并结合快速处理和低功耗的方法。最近，由于具有更高的灵活性、更短的设计周期和出色的稳定性等优势，现场可编程门阵列（FPGA）已被认为是实现神经形态解决方案的有前途的候选者。本综述旨在描述 SNN 和适合其实现的神经形态硬件平台（数字、模拟、混合和基于 FPGA）的最新进展。我们介绍了 SNN 学习的生物学背景，如神经元模型和信息编码技术，然后对 SNN 训练进行了分类。此外，我们还描述了最先进的 SNN 模拟器。此外，我们回顾并介绍了基于 FPGA 的 SNN 硬件实现。最后，我们讨论了该领域未来的一些研究方向。
