Neuromorphic computing is a relatively new discipline of computer science, where the principles of biological brain's computation and memory are used to create a new way of processing information, based on networks of spiking neurons. Those networks can be implemented as both analog and digital implementations, where for the latter, the Field Programmable Gate Arrays (FPGAs) are a frequent choice, due to their inherent flexibility, allowing the researchers to easily design hardware neuromorphic architecture (NMAs). Moreover, digital NMAs show good promise in simulating various spiking neural networks because of their inherent accuracy and resilience to noise, as opposed to analog implementations. This paper presents an overview of digital NMAs implemented on FPGAs, with a goal of providing useful references to various architectural design choices to the researchers interested in digital neuromorphic systems. We present a taxonomy of NMAs that highlights groups of distinct architectural features, their advantages and disadvantages and identify trends and predictions for the future of those architectures.

翻译：神经形态计算是计算机科学中相对较新的学科，它利用生物大脑计算与存储的原理，基于脉冲神经元网络创建了一种新的信息处理方式。这类网络既可通过模拟实现也可通过数字实现，而在数字实现中，现场可编程门阵列（FPGA）因其固有的灵活性成为常见选择——这一特性使研究人员能够便捷地设计硬件神经形态架构（NMA）。此外，与模拟实现相比，数字NMA因其固有的精度和抗噪声能力，在模拟各类脉冲神经网络方面展现出良好前景。本文综述了基于FPGA实现的数字NMA，旨在为对数字神经形态系统感兴趣的研究人员提供关于不同架构设计选择的有用参考。我们提出了一种NMA分类法，重点归纳了不同架构特征组的优势与劣势，并识别了这些架构的发展趋势及未来预测。