15 citations · 50 across the 16 of their papers we have counts for
16 papers
General-Purpose Photonic Computing Primitive for Contemporary Artificial Intelligence
Shupeng Ning, Chenghao Feng, Zhenxiang Xu +4
Photonic computing offers a promising route to accelerating artificial intelligence (AI) by providing high analog bandwidth, low latency, and low energy consumption. However, exist…
Harnessing Photonics for Machine Intelligence
Hanqing Zhu, Shupeng Ning, Hongjian Zhou +4
The exponential growth of machine-intelligence workloads is colliding with the power, memory, and interconnect limits of the post-Moore era, motivating compute substrates that scal…
ENLighten: Lighten the Transformer, Enable Efficient Optical Acceleration
Hanqing Zhu, Zhican Zhou, Shupeng Ning +4
Photonic computing has emerged as a promising substrate for accelerating the dense linear-algebra operations at the heart of AI, yet adoption for large Transformer models remains i…
Hardware-Efficient Photonic Tensor Core: Accelerating Deep Neural Networks with Structured Compression
Shupeng Ning, Hanqing Zhu, Chenghao Feng +3
The rapid growth in computing demands, particularly driven by artificial intelligence applications, has begun to exceed the capabilities of traditional electronic hardware. Optical…
PACE: Pacing Operator Learning to Accurate Optical Field Simulation for Complicated Photonic Devices
Hanqing Zhu, Wenyan Cong, Guojin Chen +4
Electromagnetic field simulation is central to designing, optimizing, and validating photonic devices and circuits. However, costly computation associated with numerical simulation…
Integrated multi-operand optical neurons for scalable and hardware-efficient deep learning
Chenghao Feng, Jiaqi Gu, Hanqing Zhu +7
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. H…
Lightening-Transformer: A Dynamically-operated Optically-interconnected Photonic Transformer Accelerator
Hanqing Zhu, Jiaqi Gu, Hanrui Wang +7
The wide adoption and significant computing resource of attention-based transformers, e.g., Vision Transformers and large language models (LLM), have driven the demand for efficien…
M3ICRO: Machine Learning-Enabled Compact Photonic Tensor Core based on PRogrammable Multi-Operand Multimode Interference
Jiaqi Gu, Hanqing Zhu, Chenghao Feng +3
Photonic computing shows promise for transformative advancements in machine learning (ML) acceleration, offering ultra-fast speed, massive parallelism, and high energy efficiency.…
A Point-of-Care Biosensor for Rapid Detection and Differentiation of COVID-19 Virus (SARS-CoV-2) and Influenza Virus Using Subwavelength Grating Micro-ring Resonator
Shupeng Ning, Hao-Chen Chang, Kang-Chieh Fan +4
In the context of continued spread of coronavirus disease 2019 (COVID-19) caused by SARS-CoV-2 and the emergence of new variants, the demand for rapid, accurate, and frequent detec…
NeurOLight: A Physics-Agnostic Neural Operator Enabling Parametric Photonic Device Simulation
Jiaqi Gu, Zhengqi Gao, Chenghao Feng +4
Optical computing is an emerging technology for next-generation efficient artificial intelligence (AI) due to its ultra-high speed and efficiency. Electromagnetic field simulation…
Fuse and Mix: MACAM-Enabled Analog Activation for Energy-Efficient Neural Acceleration
Hanqing Zhu, Keren Zhu, Jiaqi Gu +4
Analog computing has been recognized as a promising low-power alternative to digital counterparts for neural network acceleration. However, conventional analog computing is mainly…
ELight: Enabling Efficient Photonic In-Memory Neurocomputing with Life Enhancement
Hanqing Zhu, Jiaqi Gu, Chenghao Feng +4
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 nea…
ADEPT: Automatic Differentiable DEsign of Photonic Tensor Cores
Jiaqi Gu, Hanqing Zhu, Chenghao Feng +5
Photonic tensor cores (PTCs) are essential building blocks for optical artificial intelligence (AI) accelerators based on programmable photonic integrated circuits. PTCs can achiev…
A compact butterfly-style silicon photonic-electronic neural chip for hardware-efficient deep learning
Chenghao Feng, Jiaqi Gu, Hanqing Zhu +4
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. Previous…
L2ight: Enabling On-Chip Learning for Optical Neural Networks via Efficient in-situ Subspace Optimization
Jiaqi Gu, Hanqing Zhu, Chenghao Feng +3
Silicon-photonics-based optical neural network (ONN) is a promising hardware platform that could represent a paradigm shift in efficient AI with its CMOS-compatibility, flexibility…
Towards Memory-Efficient Neural Networks via Multi-Level in situ Generation
Jiaqi Gu, Hanqing Zhu, Chenghao Feng +4
Deep neural networks (DNN) have shown superior performance in a variety of tasks. As they rapidly evolve, their escalating computation and memory demands make it challenging to dep…