90 citations · 271 across the 49 of their papers we have counts for
9 papers · 1 filter
Fast and Accurate FSA System Using ELBERT: An Efficient and Lightweight BERT
Siyuan Lu, Chenchen Zhou, Keli Xie +2
With the development of deep learning and Transformer-based pre-trained models like BERT, the accuracy of many NLP tasks has been dramatically improved. However, the large number o…
ViTALiTy: Unifying Low-rank and Sparse Approximation for Vision Transformer Acceleration with a Linear Taylor Attention
Jyotikrishna Dass, Shang Wu, Huihong Shi +4
Vision Transformer (ViT) has emerged as a competitive alternative to convolutional neural networks for various computer vision applications. Specifically, ViT multi-head attention…
An Efficient FPGA-based Accelerator for Deep Forest
Mingyu Zhu, Jiapeng Luo, Wendong Mao +1
Deep Forest is a prominent machine learning algorithm known for its high accuracy in forecasting. Compared with deep neural networks, Deep Forest has almost no multiplication opera…
Accelerate Three-Dimensional Generative Adversarial Networks Using Fast Algorithm
Ziqi Su, Wendong Mao, Zhongfeng Wang +3
Three-dimensional generative adversarial networks (3D-GAN) have attracted widespread attention in three-dimension (3D) visual tasks. 3D deconvolution (DeConv), as an important comp…
An Efficient FPGA Accelerator for Point Cloud
Zilun Wang, Wendong Mao, Peixiang Yang +2
Deep learning-based point cloud processing plays an important role in various vision tasks, such as autonomous driving, virtual reality (VR), and augmented reality (AR). The subman…
BEBERT: Efficient and Robust Binary Ensemble BERT
Jiayi Tian, Chao Fang, Haonan Wang +1
Pre-trained BERT models have achieved impressive accuracy on natural language processing (NLP) tasks. However, their excessive amount of parameters hinders them from efficient depl…
NASA: Neural Architecture Search and Acceleration for Hardware Inspired Hybrid Networks
Huihong Shi, Haoran You, Yang Zhao +2
Multiplication is arguably the most cost-dominant operation in modern deep neural networks (DNNs), limiting their achievable efficiency and thus more extensive deployment in resour…
An Algorithm-Hardware Co-Optimized Framework for Accelerating N:M Sparse Transformers
Chao Fang, Aojun Zhou, Zhongfeng Wang
The Transformer has been an indispensable staple in deep learning. However, for real-life applications, it is very challenging to deploy efficient Transformers due to immense param…
GANDSE: Generative Adversarial Network based Design Space Exploration for Neural Network Accelerator Design
Lang Feng, Wenjian Liu, Chuliang Guo +3
With the popularity of deep learning, the hardware implementation platform of deep learning has received increasing interest. Unlike the general purpose devices, e.g., CPU, or GPU,…