activity
20192026
most citedPatDNN: Achieving Real-Time DNN Execution on Mobile Devices with Pattern-based Weight Pruning

211 citations · 474 across the 38 of their papers we have counts for

collaborators
Showing 2021Show all

10 papers · 1 filter

cs.CV2021★ 27 cited

SPViT: Enabling Faster Vision Transformers via Soft Token Pruning

Zhenglun Kong, Peiyan Dong, Xiaolong Ma +9

Recently, Vision Transformer (ViT) has continuously established new milestones in the computer vision field, while the high computation and memory cost makes its propagation in ind…

cs.LG2021★ 1 cited

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

Yifan Gong, Geng Yuan, Zheng Zhan +9

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

cs.LG2021★ 41 cited

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

Geng Yuan, Xiaolong Ma, Wei Niu +13

Recently, a new trend of exploring sparsity for accelerating neural network training has emerged, embracing the paradigm of training on the edge. This paper proposes a novel Memory…

cs.RO2021★ 1 cited

Enabling Level-4 Autonomous Driving on a Single $1k Off-the-Shelf Card

Hsin-Hsuan Sung, Yuanchao Xu, Jiexiong Guan +5

Autonomous driving is of great interest in both research and industry. The high cost has been one of the major roadblocks that slow down the development and adoption of autonomous…

cs.LG2021

GRIM: A General, Real-Time Deep Learning Inference Framework for Mobile Devices based on Fine-Grained Structured Weight Sparsity

Wei Niu, Zhengang Li, Xiaolong Ma +6

It is appealing but challenging to achieve real-time deep neural network (DNN) inference on mobile devices because even the powerful modern mobile devices are considered as ``resou…

cs.LG2021★ 14 cited

DNNFusion: Accelerating Deep Neural Networks Execution with Advanced Operator Fusion

Wei Niu, Jiexiong Guan, Yanzhi Wang +2

Deep Neural Networks (DNNs) have emerged as the core enabler of many major applications on mobile devices. To achieve high accuracy, DNN models have become increasingly deep with h…

eess.IV2021★ 4 cited

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

Zheng Zhan, Yifan Gong, Pu Zhao +9

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 learni…

cs.CV2021

Achieving Real-Time Object Detection on MobileDevices with Neural Pruning Search

Pu Zhao, Wei Niu, Geng Yuan +4

Object detection plays an important role in self-driving cars for security development. However, mobile systems on self-driving cars with limited computation resources lead to diff…

cs.CV2021★ 3 cited

Towards Fast and Accurate Multi-Person Pose Estimation on Mobile Devices

Xuan Shen, Geng Yuan, Wei Niu +5

The rapid development of autonomous driving, abnormal behavior detection, and behavior recognition makes an increasing demand for multi-person pose estimation-based applications, e…

cs.LG2021

A Compression-Compilation Framework for On-mobile Real-time BERT Applications

Wei Niu, Zhenglun Kong, Geng Yuan +7

Transformer-based deep learning models have increasingly demonstrated high accuracy on many natural language processing (NLP) tasks. In this paper, we propose a compression-compila…