15 citations · 28 across the 10 of their papers we have counts for
10 papers
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…
Rethinking Graph Neural Networks for the Graph Coloring Problem
Wei Li, Ruxuan Li, Yuzhe Ma +3
Graph coloring, a classical and critical NP-hard problem, is the problem of assigning connected nodes as different colors as possible. However, we observe that state-of-the-art GNN…
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…
Delving into Effective Gradient Matching for Dataset Condensation
Zixuan Jiang, Jiaqi Gu, Mingjie Liu +1
As deep learning models and datasets rapidly scale up, network training is extremely time-consuming and resource-costly. Instead of training on the entire dataset, learning with a…
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…