The development of memristive device technologies has reached a level of maturity to enable the design of complex and large-scale hybrid memristive-CMOS neural processing systems. These systems offer promising solutions for implementing novel in-memory computing architectures for machine learning and data analysis problems. We argue that they are also ideal building blocks for the integration in neuromorphic electronic circuits suitable for ultra-low power brain-inspired sensory processing systems, therefore leading to the innovative solutions for always-on edge-computing and Internet-of-Things (IoT) applications. Here we present a recipe for creating such systems based on design strategies and computing principles inspired by those used in mammalian brains. We enumerate the specifications and properties of memristive devices required to support always-on learning in neuromorphic computing systems and to minimize their power consumption. Finally, we discuss in what cases such neuromorphic systems can complement conventional processing ones and highlight the importance of exploiting the physics of both the memristive devices and of the CMOS circuits interfaced to them.

中文翻译：

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创建理想的混合忆阻-CMOS 神经形态处理系统的方法

忆阻器件技术的发展已经达到了能够设计复杂和大规模混合忆阻-CMOS 神经处理系统的成熟水平。这些系统为实现用于机器学习和数据分析问题的新型内存计算架构提供了有前景的解决方案。我们认为，它们也是集成神经形态电子电路的理想构建块，适用于超低功耗大脑启发的感觉处理系统，从而为永远在线的边缘计算和物联网 (IoT) 提供创新解决方案) 应用程序。在这里，我们提出了一种基于设计策略和计算原理创建此类系统的方法，这些原理和计算原理受到哺乳动物大脑中使用的启发。我们列举了在神经形态计算系统中支持永远在线学习并最小化其功耗所需的忆阻设备的规格和属性。最后，我们讨论在什么情况下这种神经形态系统可以补充传统的处理系统，并强调利用忆阻器件和与它们接口的 CMOS 电路的物理特性的重要性。