182 citations · 364 across the 29 of their papers we have counts for
10 papers · 1 filter
TeMPO: Efficient Time-Multiplexed Dynamic Photonic Tensor Core for Edge AI with Compact Slow-Light Electro-Optic Modulator
Meng Zhang, Dennis Yin, Nicholas Gangi +4
Electronic-photonic computing systems offer immense potential in energy-efficient artificial intelligence (AI) acceleration tasks due to the superior computing speed and efficiency…
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.…
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…
RobustAnalog: Fast Variation-Aware Analog Circuit Design Via Multi-task RL
Wei Shi, Hanrui Wang, Jiaqi Gu +4
Analog/mixed-signal circuit design is one of the most complex and time-consuming stages in the whole chip design process. Due to various process, voltage, and temperature (PVT) var…
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…
Efficient On-Chip Learning for Optical Neural Networks Through Power-Aware Sparse Zeroth-Order Optimization
Jiaqi Gu, Chenghao Feng, Zheng Zhao +3
Optical neural networks (ONNs) have demonstrated record-breaking potential in high-performance neuromorphic computing due to their ultra-high execution speed and low energy consump…