On-chip large-scale all-optical interconnect for ultra-low-latency deep neural network inference

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ID: 314091
2026
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Abstract
Abstract As artificial intelligence (AI) models continue to expand rapidly and evolve at an accelerating pace, the sustainability of scaling law faces challenges as computational resources cannot expand indefinitely. A more promising strategy is to use high-performance interconnections to compose multiple modest-capacity computing chips into a high-efficiency system, enabling comparable capability to that of much larger resource-intensive systems. However, an effective on-chip communication and network that provides high bandwidth, low latency, and large-scale parallelism to accelerate distributed computation is still lacking. Here, we address this challenge by proposing an on-chip, all-optical-interconnect-based hybrid optoelectronic distributed computing system, achieving two orders of magnitude lower inference latency than a single graphics processing unit (GPU) while using only one-ninth of its computational resources. The 400-gigabits per second (Gbps) silicon photonic transceiver chips can provide high-speed and error-free optical input/output (I/O) for processing chips, and an ultra-low-loss (≤ 5 dB at 1300 nm), non-blocking 16×16 optical switch chip can further scale out the all-optical network for massive and flexible interconnection. As a validation, we configure the system to realize optical pipeline parallelism (OPP) and execute a five-layer convolutional neural network (CNN) denoising model, which processes a total of 1,000 images of 32,768 bits each in only 105.16 µs. These results convincingly illustrate a transformative pathway toward future high-efficiency computing architectures.
Reference Key
openalex_W7161719461 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Zihan Tao, Yan Zhou, Weizhen Yu, Huajin Chang, Hao Wu, Ailian Cheng, Qian Hong, Yonglin Tang, Xiaolin Bian, Lingwei Meng, Li Li, Gaoang Shen, Cheng Zhang, Yongguang Huang, Wenhua Wang, Haowen Shu, Xingjun Wang
Journal national science review
Year 2026
DOI
10.1093/nsr/nwag282
URL
https://doi.org/10.1093/nsr/nwag282
Keywords Keywords not found

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