no code implementations • 31 May 2023 • Chenghao Feng, Jiaqi Gu, Hanqing Zhu, Rongxing Tang, Shupeng Ning, May Hlaing, Jason Midkiff, Sourabh Jain, David Z. Pan, Ray T. Chen
The optical neural network (ONN) is a promising hardware platform for next-generation neuromorphic computing due to its high parallelism, low latency, and low energy consumption.
1 code implementation • 31 May 2023 • Jiaqi Gu, Hanqing Zhu, Chenghao Feng, Zixuan Jiang, Ray T. Chen, David Z. Pan
The programmable MOMMI leverages the intrinsic light propagation principle, providing a single-device programmable matrix unit beyond the conventional computing paradigm of one multiply-accumulate (MAC) operation per device.
1 code implementation • 19 Sep 2022 • Jiaqi Gu, Zhengqi Gao, Chenghao Feng, Hanqing Zhu, Ray T. Chen, Duane S. Boning, David Z. Pan
In this work, for the first time, a physics-agnostic neural operator-based framework, dubbed NeurOLight, is proposed to learn a family of frequency-domain Maxwell PDEs for ultra-fast parametric photonic device simulation.
no code implementations • 15 Dec 2021 • Hanqing Zhu, Jiaqi Gu, Chenghao Feng, Mingjie Liu, Zixuan Jiang, Ray T. Chen, David Z. Pan
With the recent advances in optical phase change material (PCM), photonic in-memory neurocomputing has demonstrated its superiority in optical neural network (ONN) designs with near-zero static power consumption, time-of-light latency, and compact footprint.
1 code implementation • 11 Nov 2021 • Chenghao Feng, Jiaqi Gu, Hanqing Zhu, Zhoufeng Ying, Zheng Zhao, David Z. Pan, Ray T. Chen
The optical neural network (ONN) is a promising hardware platform for next-generation neurocomputing due to its high parallelism, low latency, and low energy consumption.
1 code implementation • NeurIPS 2021 • Jiaqi Gu, Hanqing Zhu, Chenghao Feng, Zixuan Jiang, Ray T. Chen, David Z. Pan
In this work, we propose a closed-loop ONN on-chip learning framework L2ight to enable scalable ONN mapping and efficient in-situ learning.
1 code implementation • 25 Aug 2021 • Jiaqi Gu, Hanqing Zhu, Chenghao Feng, Mingjie Liu, Zixuan Jiang, Ray T. Chen, David Z. Pan
Deep neural networks (DNN) have shown superior performance in a variety of tasks.
1 code implementation • IEEE Design, Automation & Test in Europe Conference & Exhibition (DATE) 2021 • Jiaqi Gu, Chenghao Feng, Zheng Zhao, Zhoufeng Ying, Mingjie Liu, Ray T. Chen, David Z. Pan
Optical neural networks (ONNs) have demonstrated promising potentials for next-generation artificial intelligence acceleration with ultra-low latency, high bandwidth, and low energy consumption.
1 code implementation • 21 Dec 2020 • Jiaqi Gu, Chenghao Feng, Zheng Zhao, Zhoufeng Ying, Ray T. Chen, David Z. Pan
Optical neural networks (ONNs) have demonstrated record-breaking potential in high-performance neuromorphic computing due to their ultra-high execution speed and low energy consumption.
