most citedHigh-precision programming of large-scale ring resonator circuits with minimal pre-calibration

9 citations · 15 across the 5 of their papers we have counts for

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physics.optics2025★ 1 cited

Beyond Terabit/s Integrated Neuromorphic Photonic Processor for DSP-Free Optical Interconnects

Benshan Wang, Qiarong Xiao, Tengji Xu +5

The rapid expansion of generative AI drives unprecedented demands for high-performance computing. Training large-scale AI models now requires vast interconnected GPU clusters acros…

physics.optics2024★ 2 cited

Online training and pruning of multi-wavelength photonic neural networks

Jiawei Zhang, Weipeng Zhang, Tengji Xu +5

CMOS-compatible photonic integrated circuits (PICs) are emerging as a promising platform in artificial intelligence (AI) computing. Owing to the compact footprint of microring reso…

physics.optics2024★ 2 cited

Perfecting Imperfect Physical Neural Networks with Transferable Robustness using Sharpness-Aware Training

Tengji Xu, Zeyu Luo, Shaojie Liu +5

AI models are essential in science and engineering, but recent advances are pushing the limits of traditional digital hardware. To address these limitations, physical neural networ…

physics.optics2024★ 9 cited

High-precision programming of large-scale ring resonator circuits with minimal pre-calibration

Shaojie Liu, Tengji Xu, Benshan Wang +3

Microring resonators (MRRs) are essential components in large-scale photonic integrated circuits (PICs), but programming these circuits with high precision and efficiency remains a…

physics.optics2024★ 1 cited

Control-free and efficient integrated photonic neural networks via hardware-aware training and pruning

Tengji Xu, Weipeng Zhang, Jiawei Zhang +8

Integrated photonic neural networks (PNNs) are at the forefront of AI computing, leveraging on light's unique properties, such as large bandwidth, low latency, and potentially low…