Physical reservoir computing is a form of neuromorphic computing that harvests the dynamic properties of materials for high-efficiency computing. A wide range of physical systems can be used to implement this approach, including electronic, optical and mechanical devices. Electronics can, in particular, provide mixed-signal and fully analogue systems, and could be used to deliver large-scale implementations. Here we examine the development of physical reservoir computing with emerging electronics. We discuss the different architectures, physical nodes, and input and output layers of electrical reservoir computing. We also explore performance benchmarks and the competitiveness of different implementations. Finally, we consider the future development of the technology and highlight challenges that need to be addressed for it to deliver practical applications.

物理储层计算是神经形态计算的一种形式，它收集材料的动态特性以进行高效计算。可以使用多种物理系统来实现这种方法，包括电子、光学和机械设备。电子设备尤其可以提供混合信号和全模拟系统，并可用于大规模实施。在这里，我们研究了新兴电子学的物理储层计算的发展。我们讨论电储层计算的不同架构、物理节点以及输入和输出层。我们还探索性能基准和不同实施的竞争力。最后，我们考虑了该技术的未来发展，并强调了该技术实现实际应用需要解决的挑战。