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 new alternative brain-inspired computing architectures that promise to achieve the flexibility and computational efficiency of biological neural processing systems. Within this context, neuromorphic intelligence represents a paradigm shift in computing based on the implementation of spiking neural network architectures tightly co-locating processing and memory. In this paper, we provide a comprehensive overview of the field, highlighting the different levels of granularity present in existing silicon implementations, comparing approaches that aim at replicating natural intelligence (bottom-up) versus those that aim at solving practical artificial intelligence applications (top-down), and assessing the benefits of the different circuit design styles used to achieve these goals. 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. Next, we highlight the key tradeoffs for each of the bottom-up and top-down approaches, survey their silicon implementations, and carry out detailed comparative analyses to extract design guidelines. Finally, we identify both necessary synergies and missing elements required to achieve a competitive advantage for neuromorphic edge computing over conventional machine-learning accelerators, and outline the key elements for a framework toward neuromorphic intelligence.

中文翻译：

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自下而上和自上而下的神经处理系统设计：神经形态智能作为自然智能和人工智能的融合

虽然摩尔定律推动了指数计算能力的预期，但其即将结束需要新的途径来提高整体系统性能。这些途径之一是探索新的替代大脑启发的计算架构，这些架构有望实现生物神经处理系统的灵活性和计算效率。在这种情况下，神经形态智能代表了基于尖峰神经网络架构的实现的计​​算范式转变，该架构紧密地协同定位处理和内存。在本文中，我们对该领域进行了全面概述，重点介绍了现有硅实现中存在的不同粒度级别，比较旨在复制自然智能（自下而上）的方法与旨在解决实际人工智能应用（自上而下）的方法，并评估用于实现这些目标的不同电路设计风格的好处。首先，我们介绍了模拟、混合信号和数字电路设计风格，通过时分复用、内存计算和新设备来确定处理和内存之间的边界。接下来，我们重点介绍了每种自下而上和自上而下方法的关键权衡，调查了它们的芯片实现，并进行了详细的比较分析以提取设计指南。最后，我们确定了实现神经形态边缘计算相对于传统机器学习加速器的竞争优势所需的必要协同作用和缺失元素，