Thakur Chetan Singh, Molin Jamal Lottier, Cauwenberghs Gert, Indiveri Giacomo, Kumar Kundan, Qiao Ning, Schemmel Johannes, Wang Runchun, Chicca Elisabetta, Olson Hasler Jennifer, Seo Jae-Sun, Yu Shimeng, Cao Yu, van Schaik André, Etienne-Cummings Ralph
Department of Electronic Systems Engineering, Indian Institute of Science, Bangalore, India.
Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, United States.
Front Neurosci. 2018 Dec 3;12:891. doi: 10.3389/fnins.2018.00891. eCollection 2018.
Neuromorphic engineering (NE) encompasses a diverse range of approaches to information processing that are inspired by neurobiological systems, and this feature distinguishes neuromorphic systems from conventional computing systems. The brain has evolved over billions of years to solve difficult engineering problems by using efficient, parallel, low-power computation. The goal of NE is to design systems capable of brain-like computation. Numerous large-scale neuromorphic projects have emerged recently. This interdisciplinary field was listed among the top 10 technology breakthroughs of 2014 by the MIT Technology Review and among the top 10 emerging technologies of 2015 by the World Economic Forum. NE has two-way goals: one, a scientific goal to understand the computational properties of biological neural systems by using models implemented in integrated circuits (ICs); second, an engineering goal to exploit the known properties of biological systems to design and implement efficient devices for engineering applications. Building hardware neural emulators can be extremely useful for simulating large-scale neural models to explain how intelligent behavior arises in the brain. The principal advantages of neuromorphic emulators are that they are highly energy efficient, parallel and distributed, and require a small silicon area. Thus, compared to conventional CPUs, these neuromorphic emulators are beneficial in many engineering applications such as for the porting of deep learning algorithms for various recognitions tasks. In this review article, we describe some of the most significant neuromorphic spiking emulators, compare the different architectures and approaches used by them, illustrate their advantages and drawbacks, and highlight the capabilities that each can deliver to neural modelers. This article focuses on the discussion of large-scale emulators and is a continuation of a previous review of various neural and synapse circuits (Indiveri et al., 2011). We also explore applications where these emulators have been used and discuss some of their promising future applications.
神经形态工程（NE）涵盖了一系列受神经生物学系统启发的信息处理方法，这一特性使神经形态系统有别于传统计算系统。大脑经过数十亿年的进化，通过高效、并行、低功耗的计算来解决复杂的工程问题。神经形态工程的目标是设计出能够进行类脑计算的系统。最近涌现出了许多大规模的神经形态项目。这个跨学科领域被《麻省理工科技评论》列为2014年十大技术突破之一，并被世界经济论坛列为2015年十大新兴技术之一。神经形态工程有两个目标：其一，是科学目标，即通过使用集成电路（IC）中实现的模型来理解生物神经系统的计算特性；其二，是工程目标，即利用生物系统的已知特性来设计和实现用于工程应用的高效设备。构建硬件神经模拟器对于模拟大规模神经模型以解释大脑中智能行为的产生极为有用。神经形态模拟器的主要优点是它们具有高能效、并行和分布式的特点，并且所需的硅面积小。因此，与传统CPU相比，这些神经形态模拟器在许多工程应用中都很有益，比如用于移植各种识别任务的深度学习算法。在这篇综述文章中，我们描述了一些最重要的神经形态脉冲模拟器，比较了它们使用的不同架构和方法，阐述了它们的优缺点，并突出了每种模拟器能够为神经建模人员提供的能力。本文重点讨论大规模模拟器，是之前对各种神经和突触电路的综述（因迪维里等人，2011年）的延续。我们还探讨了这些模拟器的应用领域，并讨论了它们一些有前景的未来应用。
