8 citations · 9 across the 3 of their papers we have counts for
9 papers
Low-Rank+Sparse Tensor Compression for Neural Networks
Cole Hawkins, Haichuan Yang, Meng Li +2
Low-rank tensor compression has been proposed as a promising approach to reduce the memory and compute requirements of neural networks for their deployment on edge devices. Tensor…
Noisy Training Improves E2E ASR for the Edge
Dilin Wang, Yuan Shangguan, Haichuan Yang +6
Automatic speech recognition (ASR) has become increasingly ubiquitous on modern edge devices. Past work developed streaming End-to-End (E2E) all-neural speech recognizers that can…
GAN Slimming: All-in-One GAN Compression by A Unified Optimization Framework
Haotao Wang, Shupeng Gui, Haichuan Yang +2
Generative adversarial networks (GANs) have gained increasing popularity in various computer vision applications, and recently start to be deployed to resource-constrained mobile d…
Automatic Neural Network Compression by Sparsity-Quantization Joint Learning: A Constrained Optimization-based Approach
Haichuan Yang, Shupeng Gui, Yuhao Zhu +1
Deep Neural Networks (DNNs) are applied in a wide range of usecases. There is an increased demand for deploying DNNs on devices that do not have abundant resources such as memory a…
Model Compression with Adversarial Robustness: A Unified Optimization Framework
Shupeng Gui, Haotao Wang, Chen Yu +3
Deep model compression has been extensively studied, and state-of-the-art methods can now achieve high compression ratios with minimal accuracy loss. This paper studies model compr…
ECC: Platform-Independent Energy-Constrained Deep Neural Network Compression via a Bilinear Regression Model
Haichuan Yang, Yuhao Zhu, Ji Liu
Many DNN-enabled vision applications constantly operate under severe energy constraints such as unmanned aerial vehicles, Augmented Reality headsets, and smartphones. Designing DNN…