211 citations · 474 across the 38 of their papers we have counts for
13 papers · 1 filter
Achieving Real-Time LiDAR 3D Object Detection on a Mobile Device
Pu Zhao, Wei Niu, Geng Yuan +7
3D object detection is an important task, especially in the autonomous driving application domain. However, it is challenging to support the real-time performance with the limited…
NPAS: A Compiler-aware Framework of Unified Network Pruning and Architecture Search for Beyond Real-Time Mobile Acceleration
Zhengang Li, Geng Yuan, Wei Niu +13
With the increasing demand to efficiently deploy DNNs on mobile edge devices, it becomes much more important to reduce unnecessary computation and increase the execution speed. Pri…
ClickTrain: Efficient and Accurate End-to-End Deep Learning Training via Fine-Grained Architecture-Preserving Pruning
Chengming Zhang, Geng Yuan, Wei Niu +8
Convolutional neural networks (CNNs) are becoming increasingly deeper, wider, and non-linear because of the growing demand on prediction accuracy and analysis quality. The wide and…
Real-Time Execution of Large-scale Language Models on Mobile
Wei Niu, Zhenglun Kong, Geng Yuan +7
Pre-trained large-scale language models have increasingly demonstrated high accuracy on many natural language processing (NLP) tasks. However, the limited weight storage and comput…
YOLObile: Real-Time Object Detection on Mobile Devices via Compression-Compilation Co-Design
Yuxuan Cai, Hongjia Li, Geng Yuan +5
The rapid development and wide utilization of object detection techniques have aroused attention on both accuracy and speed of object detectors. However, the current state-of-the-a…
RT3D: Achieving Real-Time Execution of 3D Convolutional Neural Networks on Mobile Devices
Wei Niu, Mengshu Sun, Zhengang Li +7
Mobile devices are becoming an important carrier for deep learning tasks, as they are being equipped with powerful, high-end mobile CPUs and GPUs. However, it is still a challengin…
Towards Real-Time DNN Inference on Mobile Platforms with Model Pruning and Compiler Optimization
Wei Niu, Pu Zhao, Zheng Zhan +3
High-end mobile platforms rapidly serve as primary computing devices for a wide range of Deep Neural Network (DNN) applications. However, the constrained computation and storage re…
A Privacy-Preserving-Oriented DNN Pruning and Mobile Acceleration Framework
Yifan Gong, Zheng Zhan, Zhengang Li +8
Weight pruning of deep neural networks (DNNs) has been proposed to satisfy the limited storage and computing capability of mobile edge devices. However, previous pruning methods ma…
BLK-REW: A Unified Block-based DNN Pruning Framework using Reweighted Regularization Method
Xiaolong Ma, Zhengang Li, Yifan Gong +8
Accelerating DNN execution on various resource-limited computing platforms has been a long-standing problem. Prior works utilize l1-based group lasso or dynamic regularization such…
RTMobile: Beyond Real-Time Mobile Acceleration of RNNs for Speech Recognition
Peiyan Dong, Siyue Wang, Wei Niu +8
Recurrent neural networks (RNNs) based automatic speech recognition has nowadays become prevalent on mobile devices such as smart phones. However, previous RNN compression techniqu…
Attentional Speech Recognition Models Misbehave on Out-of-domain Utterances
Phillip Keung, Wei Niu, Yichao Lu +2
We discuss the problem of echographic transcription in autoregressive sequence-to-sequence attentional architectures for automatic speech recognition, where a model produces very l…
PatDNN: Achieving Real-Time DNN Execution on Mobile Devices with Pattern-based Weight Pruning
Wei Niu, Xiaolong Ma, Sheng Lin +5
With the emergence of a spectrum of high-end mobile devices, many applications that formerly required desktop-level computation capability are being transferred to these devices. H…
An Image Enhancing Pattern-based Sparsity for Real-time Inference on Mobile Devices
Xiaolong Ma, Wei Niu, Tianyun Zhang +8
Weight pruning has been widely acknowledged as a straightforward and effective method to eliminate redundancy in Deep Neural Networks (DNN), thereby achieving acceleration on vario…