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PUBLIC SUMMARY
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Advancements in physical reservoir computing (PRC) for low-latency, energy-efficient Edge AI.
Highlighting power efficiency, and scalability benefits, among neuromorphic hardware platforms.
Commented challenges like device variability and system stability in heterogeneous integration.
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ABSTRACT
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. -
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REFERENCES
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Liu J., Feng G., Li W., et al. (2025). Physical reservoir computing for Edge AI applications. The Innovation Materials 3:100127. https://doi.org/10.59717/j.xinn-mater.2025.100127
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Liu J., Feng G., Li W., et al. (2025). Physical reservoir computing for Edge AI applications. The Innovation Materials 3:100127. https://doi.org/10.59717/j.xinn-mater.2025.100127
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Figure 1.
Key milestones in the research of RC
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Figure 2.
The influence of linear separability and time-adaptive characteristics on RC
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Figure 3.
General Process of Speech Command Recognition Using PRC Systems
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Figure 4.
Time-Series Prediction Using a PRC System
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Figure 5.
Schematic diagram, operational principles, suitable fields, pros and cons. of Various Neuromorphic Devices for PRC Systems
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Figure 6.
Various Spatial Architectures of PRC Systems and Their Physical Realizations
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Figure 7.
Conceptual Framework and Physical Realization of Various Hardware Readout Networks in PRC Systems
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Figure 8.
Applicability of PRC Systems Across Various Application Domains
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Figure 9.
Schematic framework illustrating the synergistic development of PRC algorithms and hardware architectures.
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Figure 10.
Five stages of RC development and future application domains for PRC each domain is illustrated with three specific application scenarios, highlighting RC’s potential impact across diverse fields.
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