Search Results for author: Zheng Zhan

Found 13 papers, 2 papers with code

All-in-One: A Highly Representative DNN Pruning Framework for Edge Devices with Dynamic Power Management

no code implementations9 Dec 2022 Yifan Gong, Zheng Zhan, Pu Zhao, Yushu Wu, Chao Wu, Caiwen Ding, Weiwen Jiang, Minghai Qin, Yanzhi Wang

By re-configuring the model to the corresponding pruning ratio for a specific execution frequency (and voltage), we are able to achieve stable inference speed, i. e., keeping the difference in speed performance under various execution frequencies as small as possible.

Management

SparCL: Sparse Continual Learning on the Edge

no code implementations20 Sep 2022 Zifeng Wang, Zheng Zhan, Yifan Gong, Geng Yuan, Wei Niu, Tong Jian, Bin Ren, Stratis Ioannidis, Yanzhi Wang, Jennifer Dy

SparCL achieves both training acceleration and accuracy preservation through the synergy of three aspects: weight sparsity, data efficiency, and gradient sparsity.

Continual Learning

Compiler-Aware Neural Architecture Search for On-Mobile Real-time Super-Resolution

1 code implementation25 Jul 2022 Yushu Wu, Yifan Gong, Pu Zhao, Yanyu Li, Zheng Zhan, Wei Niu, Hao Tang, Minghai Qin, Bin Ren, Yanzhi Wang

Instead of measuring the speed on mobile devices at each iteration during the search process, a speed model incorporated with compiler optimizations is leveraged to predict the inference latency of the SR block with various width configurations for faster convergence.

Neural Architecture Search SSIM +1

Automatic Mapping of the Best-Suited DNN Pruning Schemes for Real-Time Mobile Acceleration

no code implementations22 Nov 2021 Yifan Gong, Geng Yuan, Zheng Zhan, Wei Niu, Zhengang Li, Pu Zhao, Yuxuan Cai, Sijia Liu, Bin Ren, Xue Lin, Xulong Tang, Yanzhi Wang

Weight pruning is an effective model compression technique to tackle the challenges of achieving real-time deep neural network (DNN) inference on mobile devices.

Model Compression

MEST: Accurate and Fast Memory-Economic Sparse Training Framework on the Edge

1 code implementation NeurIPS 2021 Geng Yuan, Xiaolong Ma, Wei Niu, Zhengang Li, Zhenglun Kong, Ning Liu, Yifan Gong, Zheng Zhan, Chaoyang He, Qing Jin, Siyue Wang, Minghai Qin, Bin Ren, Yanzhi Wang, Sijia Liu, Xue Lin

Systematical evaluation on accuracy, training speed, and memory footprint are conducted, where the proposed MEST framework consistently outperforms representative SOTA works.

Achieving on-Mobile Real-Time Super-Resolution with Neural Architecture and Pruning Search

no code implementations ICCV 2021 Zheng Zhan, Yifan Gong, Pu Zhao, Geng Yuan, Wei Niu, Yushu Wu, Tianyun Zhang, Malith Jayaweera, David Kaeli, Bin Ren, Xue Lin, Yanzhi Wang

Though recent years have witnessed remarkable progress in single image super-resolution (SISR) tasks with the prosperous development of deep neural networks (DNNs), the deep learning methods are confronted with the computation and memory consumption issues in practice, especially for resource-limited platforms such as mobile devices.

Image Super-Resolution Neural Architecture Search +2

Towards Real-Time DNN Inference on Mobile Platforms with Model Pruning and Compiler Optimization

no code implementations22 Apr 2020 Wei Niu, Pu Zhao, Zheng Zhan, Xue Lin, Yanzhi Wang, Bin Ren

High-end mobile platforms rapidly serve as primary computing devices for a wide range of Deep Neural Network (DNN) applications.

Style Transfer Super-Resolution

A Unified DNN Weight Compression Framework Using Reweighted Optimization Methods

no code implementations12 Apr 2020 Tianyun Zhang, Xiaolong Ma, Zheng Zhan, Shanglin Zhou, Minghai Qin, Fei Sun, Yen-Kuang Chen, Caiwen Ding, Makan Fardad, Yanzhi Wang

To address the large model size and intensive computation requirement of deep neural networks (DNNs), weight pruning techniques have been proposed and generally fall into two categories, i. e., static regularization-based pruning and dynamic regularization-based pruning.

A Privacy-Preserving-Oriented DNN Pruning and Mobile Acceleration Framework

no code implementations13 Mar 2020 Yifan Gong, Zheng Zhan, Zhengang Li, Wei Niu, Xiaolong Ma, Wenhao Wang, Bin Ren, Caiwen Ding, Xue Lin, Xiao-Lin Xu, Yanzhi Wang

Weight pruning of deep neural networks (DNNs) has been proposed to satisfy the limited storage and computing capability of mobile edge devices.

Model Compression Privacy Preserving

SS-Auto: A Single-Shot, Automatic Structured Weight Pruning Framework of DNNs with Ultra-High Efficiency

no code implementations23 Jan 2020 Zhengang Li, Yifan Gong, Xiaolong Ma, Sijia Liu, Mengshu Sun, Zheng Zhan, Zhenglun Kong, Geng Yuan, Yanzhi Wang

Structured weight pruning is a representative model compression technique of DNNs for hardware efficiency and inference accelerations.

Model Compression

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