40 citations · 67 across the 9 of their papers we have counts for
9 papers
Solutions of the singular Yamabe problem near singular boundaries
Weiming Shen, Zhehui Wang, Jiongduo Xie
In this paper, we investigate the asymptotic behaviors of solutions to the singular Yamabe problem with negative constant scalar curvature near singular boundaries and derive optim…
Coflex: Enhancing HW-NAS with Sparse Gaussian Processes for Efficient and Scalable DNN Accelerator Design
Yinhui Ma, Tomomasa Yamasaki, Zhehui Wang +2
Hardware-Aware Neural Architecture Search (HW-NAS) is an efficient approach to automatically co-optimizing neural network performance and hardware energy efficiency, making it part…
Is Quantum Optimization Ready? An Effort Towards Neural Network Compression using Adiabatic Quantum Computing
Zhehui Wang, Benjamin Chen Ming Choong, Tian Huang +4
Quantum optimization is the most mature quantum computing technology to date, providing a promising approach towards efficiently solving complex combinatorial problems. Methods suc…
RBFleX-NAS: Training-Free Neural Architecture Search Using Radial Basis Function Kernel and Hyperparameter Detection
Tomomasa Yamasaki, Zhehui Wang, Tao Luo +2
Neural Architecture Search (NAS) is an automated technique to design optimal neural network architectures for a specific workload. Conventionally, evaluating candidate networks in…
Enabling Energy-Efficient Deployment of Large Language Models on Memristor Crossbar: A Synergy of Large and Small
Zhehui Wang, Tao Luo, Cheng Liu +3
Large language models (LLMs) have garnered substantial attention due to their promising applications in diverse domains. Nevertheless, the increasing size of LLMs comes with a sign…
Ill-posedness of the Dirichlet problem for 2D Lagrangian mean curvature equation
Wentao Cao, Zhehui Wang
We investigate the Dirichlet problem of the two dimensional Lagrangian mean curvature equation in a bounded domain. Infinitely many very weak solu…
A Resource-efficient Spiking Neural Network Accelerator Supporting Emerging Neural Encoding
Daniel Gerlinghoff, Zhehui Wang, Xiaozhe Gu +2
Spiking neural networks (SNNs) recently gained momentum due to their low-power multiplication-free computing and the closer resemblance of biological processes in the nervous syste…
Optimizing for In-memory Deep Learning with Emerging Memory Technology
Zhehui Wang, Tao Luo, Rick Siow Mong Goh +2
In-memory deep learning computes neural network models where they are stored, thus avoiding long distance communication between memory and computation units, resulting in considera…
E3NE: An End-to-End Framework for Accelerating Spiking Neural Networks with Emerging Neural Encoding on FPGAs
Daniel Gerlinghoff, Zhehui Wang, Xiaozhe Gu +2
Compiler frameworks are crucial for the widespread use of FPGA-based deep learning accelerators. They allow researchers and developers, who are not familiar with hardware engineeri…