75 citations · 112 across the 7 of their papers we have counts for
7 papers
Avoiding Premature Collapse: Adaptive Annealing for Entropy-Regularized Structural Inference
Yizhi Liu
Differentiable matching layers and residual connection paradigms, often implemented via entropy-regularized Optimal Transport (OT), serve as critical mechanisms in structural predi…
The Homogeneity Trap: Spectral Collapse in Doubly-Stochastic Deep Networks
Yizhi Liu
Doubly-stochastic matrices (DSM) are increasingly utilized in structure-preserving deep architectures -- such as Optimal Transport layers and Sinkhorn-based attention -- to enforce…
RAF: Holistic Compilation for Deep Learning Model Training
Cody Hao Yu, Haozheng Fan, Guangtai Huang +9
As deep learning is pervasive in modern applications, many deep learning frameworks are presented for deep learning practitioners to develop and train DNN models rapidly. Meanwhile…
Hidet: Task-Mapping Programming Paradigm for Deep Learning Tensor Programs
Yaoyao Ding, Cody Hao Yu, Bojian Zheng +3
As deep learning models nowadays are widely adopted by both cloud services and edge devices, reducing the latency of deep learning model inferences becomes crucial to provide effic…
Optimizing Memory-Access Patterns for Deep Learning Accelerators
Hongbin Zheng, Sejong Oh, Huiqing Wang +7
Deep learning (DL) workloads are moving towards accelerators for faster processing and lower cost. Modern DL accelerators are good at handling the large-scale multiply-accumulate o…
A Unified Optimization Approach for CNN Model Inference on Integrated GPUs
Leyuan Wang, Zhi Chen, Yizhi Liu +4
Modern deep learning applications urge to push the model inference taking place at the edge devices for multiple reasons such as achieving shorter latency, relieving the burden of…
Optimizing CNN Model Inference on CPUs
Yizhi Liu, Yao Wang, Ruofei Yu +3
The popularity of Convolutional Neural Network (CNN) models and the ubiquity of CPUs imply that better performance of CNN model inference on CPUs can deliver significant gain to a…