22 citations · 79 across the 16 of their papers we have counts for
16 papers
Performance Evaluation and Acceleration of the QTensor Quantum Circuit Simulator on GPUs
Danylo Lykov, Angela Chen, Huaxuan Chen +4
This work studies the porting and optimization of the tensor network simulator QTensor on GPUs, with the ultimate goal of simulating quantum circuits efficiently at scale on large…
PoBO: A Polynomial Bounding Method for Chance-Constrained Yield-Aware Optimization of Photonic ICs
Zichang He, Zheng Zhang
Conventional yield optimization algorithms try to maximize the success rate of a circuit under process variations. These methods often obtain a high yield but reach a design perfor…
3U-EdgeAI: Ultra-Low Memory Training, Ultra-Low BitwidthQuantization, and Ultra-Low Latency Acceleration
Yao Chen, Cole Hawkins, Kaiqi Zhang +2
The deep neural network (DNN) based AI applications on the edge require both low-cost computing platforms and high-quality services. However, the limited memory, computing resource…
On-FPGA Training with Ultra Memory Reduction: A Low-Precision Tensor Method
Kaiqi Zhang, Cole Hawkins, Xiyuan Zhang +2
Various hardware accelerators have been developed for energy-efficient and real-time inference of neural networks on edge devices. However, most training is done on high-performanc…
High-Dimensional Uncertainty Quantification via Tensor Regression with Rank Determination and Adaptive Sampling
Zichang He, Zheng Zhang
Fabrication process variations can significantly influence the performance and yield of nano-scale electronic and photonic circuits. Stochastic spectral methods have achieved great…
Quantum-Inspired Hamiltonian Monte Carlo for Bayesian Sampling
Ziming Liu, Zheng Zhang
Hamiltonian Monte Carlo (HMC) is an efficient Bayesian sampling method that can make distant proposals in the parameter space by simulating a Hamiltonian dynamical system. Despite…
Active Subspace of Neural Networks: Structural Analysis and Universal Attacks
Chunfeng Cui, Kaiqi Zhang, Talgat Daulbaev +3
Active subspace is a model reduction method widely used in the uncertainty quantification community. In this paper, we propose analyzing the internal structure and vulnerability an…
Tensor Methods for Generating Compact Uncertainty Quantification and Deep Learning Models
Chunfeng Cui, Cole Hawkins, Zheng Zhang
Tensor methods have become a promising tool to solve high-dimensional problems in the big data era. By exploiting possible low-rank tensor factorization, many high-dimensional mode…
Chance-Constrained and Yield-aware Optimization of Photonic ICs with Non-Gaussian Correlated Process Variations
Chunfeng Cui, Kaikai Liu, Zheng Zhang
Uncertainty quantification has become an efficient tool for uncertainty-aware prediction, but its power in yield-aware optimization has not been well explored from either theoretic…
Tucker Tensor Decomposition on FPGA
Kaiqi Zhang, Xiyuan Zhang, Zheng Zhang
Tensor computation has emerged as a powerful mathematical tool for solving high-dimensional and/or extreme-scale problems in science and engineering. The last decade has witnessed…
Bayesian Tensorized Neural Networks with Automatic Rank Selection
Cole Hawkins, Zheng Zhang
Tensor decomposition is an effective approach to compress over-parameterized neural networks and to enable their deployment on resource-constrained hardware platforms. However, dir…
High-Dimensional Uncertainty Quantification of Electronic and Photonic IC with Non-Gaussian Correlated Process Variations
Chunfeng Cui, Zheng Zhang
Uncertainty quantification based on generalized polynomial chaos has been used in many applications. It has also achieved great success in variation-aware design automation. Howeve…
Prediction of multi-dimensional spatial variation data via Bayesian tensor completion
Jiali Luan, Zheng Zhang
This paper presents a multi-dimensional computational method to predict the spatial variation data inside and across multiple dies of a wafer. This technique is based on tensor com…
Robust Factorization and Completion of Streaming Tensor Data via Variational Bayesian Inference
Cole Hawkins, Zheng Zhang
Streaming tensor factorization is a powerful tool for processing high-volume and multi-way temporal data in Internet networks, recommender systems and image/video data analysis. In…
Variational Bayesian Inference for Robust Streaming Tensor Factorization and Completion
Cole Hawkins, Zheng Zhang
Streaming tensor factorization is a powerful tool for processing high-volume and multi-way temporal data in Internet networks, recommender systems and image/video data analysis. Ex…
Stochastic Collocation with Non-Gaussian Correlated Process Variations: Theory, Algorithms and Applications
Chunfeng Cui, Zheng Zhang
Stochastic spectral methods have achieved great success in the uncertainty quantification of many engineering problems, including electronic and photonic integrated circuits influe…