Memory and nonlinearity play crucial roles in reservoir computing (RC) performance, with different tasks requiring different levels of these properties. However, in most physical RC systems, memory and nonlinearity are fixed or can only be changed through hardware redesign, significantly limiting the system's adaptab...
Yi-Kun Nie, Dong-Liang Wang, Bo Yang et al.· Laser & Photonics Review...· 0 citations
All-optical diffractive neural networks (DNNs) offer low latency, low energy consumption, and massive parallelism. However, their scalability in terms of network scale and multi-task processing remains limited. Here, we experimentally demonstrate a large-scale metasurface-based diffractive learning machine for angle-mu...
Ming-Cheng Luo, Jian-Min Xiong, Jia-Yong Peng et al.· 0 citations
This work redefine MRRs as programmable temporal convolution kernels by exploiting their impulse responses, enabling computation beyond the resonance linewidth, and realizes a multi-channel MRR convolution engine with 120.8 TOPS throughput and reduce the hardware complexity.
Shao-Jie Liu, Teng-Ji Xu, Ben-Shan Wang et al.· Nature Communications· 0 citations
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