19 citations · 47 across the 7 of their papers we have counts for
7 papers
NeuralStagger: Accelerating Physics-constrained Neural PDE Solver with Spatial-temporal Decomposition
Xinquan Huang, Wenlei Shi, Qi Meng +4
Neural networks have shown great potential in accelerating the solution of partial differential equations (PDEs). Recently, there has been a growing interest in introducing physics…
Efficient Adaptive Activation Rounding for Post-Training Quantization
Zhengyi Li, Cong Guo, Zhanda Zhu +5
Post-training quantization attracts increasing attention due to its convenience in deploying quantized neural networks. Although rounding-to-nearest remains the prevailing method f…
LordNet: An Efficient Neural Network for Learning to Solve Parametric Partial Differential Equations without Simulated Data
Xinquan Huang, Wenlei Shi, Xiaotian Gao +5
Neural operators, as a powerful approximation to the non-linear operators between infinite-dimensional function spaces, have proved to be promising in accelerating the solution of…
SQuant: On-the-Fly Data-Free Quantization via Diagonal Hessian Approximation
Cong Guo, Yuxian Qiu, Jingwen Leng +6
Quantization of deep neural networks (DNN) has been proven effective for compressing and accelerating DNN models. Data-free quantization (DFQ) is a promising approach without the o…
Learning Physics-Informed Neural Networks without Stacked Back-propagation
Di He, Shanda Li, Wenlei Shi +5
Physics-Informed Neural Network (PINN) has become a commonly used machine learning approach to solve partial differential equations (PDE). But, facing high-dimensional secondorder…
Learning to Rank Ace Neural Architectures via Normalized Discounted Cumulative Gain
Yuge Zhang, Quanlu Zhang, Li Lyna Zhang +4
One of the key challenges in Neural Architecture Search (NAS) is to efficiently rank the performances of architectures. The mainstream assessment of performance rankers uses rankin…
OpEvo: An Evolutionary Method for Tensor Operator Optimization
Xiaotian Gao, Cui Wei, Lintao Zhang +1
Training and inference efficiency of deep neural networks highly rely on the performance of tensor operators on hardware platforms. Manually optimizing tensor operators has limitat…