While Moore’s law has driven exponential computing power expectations, its nearing end calls for new avenues for improving the overall system performance. One of these avenues is the exploration of alternative brain-inspired computing architectures that aim at achieving the flexibility and computational efficiency of biological neural processing systems. Within this context, neuromorphic engineering represents a paradigm shift in computing based on the implementation of spiking neural network architectures in which processing and memory are tightly colocated. In this article, we provide a comprehensive overview of the field, highlighting the different levels of granularity at which this paradigm shift is realized and comparing design approaches that focus on replicating natural intelligence (bottom-up) versus those that aim at solving practical artificial intelligence applications (top-down). First, we present the analog, mixed-signal, and digital circuit design styles, identifying the boundary between processing and memory through time multiplexing, in-memory computation, and novel devices. Then, we highlight the key tradeoffs for each of the bottom-up and top-down design approaches, survey their silicon implementations, and carry out detailed comparative analyses to extract design guidelines. Finally, we identify necessary synergies and missing elements required to achieve a competitive advantage for neuromorphic systems over conventional machine-learning accelerators in edge computing applications and outline the key ingredients for a framework toward neuromorphic intelligence.

中文翻译：

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神经形态处理系统设计的自下而上和自上而下的方法：自然智能和人工智能之间的权衡和协同


虽然摩尔定律推动了计算能力指数级增长，但其即将结束需要新的途径来提高整体系统性能。其中之一是探索替代性的受大脑启发的计算架构，旨在实现生物神经处理系统的灵活性和计算效率。在此背景下，神经形态工程代表了基于尖峰神经网络架构实现的计算范式转变，其中处理和内存紧密共置。在本文中，我们对该领域进行了全面的概述，强调了实现这种范式转变的不同粒度级别，并比较了专注于复制自然智能（自下而上）的设计方法与旨在解决实际人工智能问题的设计方法应用程序（自上而下）。首先，我们介绍模拟、混合信号和数字电路设计风格，通过时间复用、内存计算和新型设备来确定处理和内存之间的边界。然后，我们重点介绍每种自下而上和自上而下设计方法的关键权衡，调查其芯片实现，并进行详细的比较分析以提取设计指南。最后，我们确定了神经形态系统在边缘计算应用中相对于传统机器学习加速器实现竞争优势所需的必要协同作用和缺失要素，并概述了神经形态智能框架的关键要素。