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 co-located. In this paper, 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.

翻译：尽管摩尔定律推动了计算能力的指数级增长预期，但其即将终结的现实要求我们探索提升系统整体性能的新途径。其中一条途径是研究受大脑启发的替代计算架构，旨在实现生物神经处理系统的灵活性与计算效率。在此背景下，神经形态工程代表了一种基于脉冲神经网络架构的计算范式转变，该架构中处理与存储紧密协同定位。本文对该领域进行全面的综述，重点阐述这一范式转变在不同粒度层级上的实现方式，并对比关注复现自然智能（自底向上）与解决实际人工智能应用（自顶向下）的设计方法。首先，我们介绍模拟、混合信号与数字电路设计风格，通过时分复用、存内计算及新型器件明确处理与存储之间的边界。随后，我们强调自底向上与自顶向下两种设计方法各自的关键权衡，调研其硅实现方案，并通过详细对比分析提取设计准则。最后，我们识别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取得竞争优势所需的必要协同效应与缺失要素，并勾勒出神经形态智能框架的关键组成部分。