3 citations · 7 across the 6 of their papers we have counts for
7 papers
Memory-aware Scheduling for Complex Wired Networks with Iterative Graph Optimization
Shuzhang Zhong, Meng Li, Yun Liang +2
Memory-aware network scheduling is becoming increasingly important for deep neural network (DNN) inference on resource-constrained devices. However, due to the complex cell-level a…
HybridNet: Dual-Branch Fusion of Geometrical and Topological Views for VLSI Congestion Prediction
Yuxiang Zhao, Zhuomin Chai, Yibo Lin +2
Accurate early congestion prediction can prevent unpleasant surprises at the routing stage, playing a crucial character in assisting designers to iterate faster in VLSI design cycl…
SCALES: Boost Binary Neural Network for Image Super-Resolution with Efficient Scalings
Renjie Wei, Zechun Liu, Yuchen Fan +3
Deep neural networks for image super-resolution (SR) have demonstrated superior performance. However, the large memory and computation consumption hinders their deployment on resou…
MPCViT: Searching for Accurate and Efficient MPC-Friendly Vision Transformer with Heterogeneous Attention
Wenxuan Zeng, Meng Li, Wenjie Xiong +5
Secure multi-party computation (MPC) enables computation directly on encrypted data and protects both data and model privacy in deep learning inference. However, existing neural ne…
CircuitNet: An Open-Source Dataset for Machine Learning Applications in Electronic Design Automation (EDA)
Zhuomin Chai, Yuxiang Zhao, Yibo Lin +3
The electronic design automation (EDA) community has been actively exploring machine learning (ML) for very large-scale integrated computer-aided design (VLSI CAD). Many studies ex…
DaSGD: Squeezing SGD Parallelization Performance in Distributed Training Using Delayed Averaging
Qinggang Zhou, Yawen Zhang, Pengcheng Li +4
The state-of-the-art deep learning algorithms rely on distributed training systems to tackle the increasing sizes of models and training data sets. Minibatch stochastic gradient de…
A Parallel Bitstream Generator for Stochastic Computing
Yawen Zhang, Runsheng Wang, Xinyue Zhang +5
Stochastic computing (SC) presents high error tolerance and low hardware cost, and has great potential in applications such as neural networks and image processing. However, the bi…