Physical reservoir computing for Edge AI applications

Reservoir computing has emerged as an efficient computational paradigm for processing temporal and dynamic data, driving advancements in neuromorphic electronics for physical implementation. This review covers the advancements in neuromorphic devices for implementing physical reservoir computing, emphasizing device-level innovations that address the challenges of low-latency, energy-efficient, multimodal physical reservoir computing implementations. The advantages, disadvantages, and core challenges of various spatial architectures for building physical reservoir computing systems are discussed. Realistic paths on algorithmic and physical implementations of the input and output layers of the system are investigated, and issues such as heterogeneous device integration, consistent readout, and system stability are analyzed. This topical review emphasizes the reconfigurability and scalability of fully analogized physical reservoir computing architectures and adaptive dynamic nodes. We discuss challenges and future directions of physical reservoir computing across algorithmic, device, architectural, and application domains. This review establishes a foundational framework and provides strategic guidance for implementing physical reservoir computing in neuromorphic edge artificial intelligent systems.