1 citations · 1 across the 14 of their papers we have counts for
15 papers
A Society of Researchers: Designing Institutions for Populations of Autonomous Research Agents
Ali Asaria, Deep Gandhi, Tony Salomone
Deployments of research agents are moving to populations of thousands that share one pool of compute, while most current systems organize one project at a time or leave the populat…
Deep Lookup Network
Yulan Guo, Longguang Wang, Wendong Mao +4
Convolutional neural networks are constructed with massive operations with different types and are highly computationally intensive. Among these operations, multiplication operatio…
StripDet: Strip Attention-Based Lightweight 3D Object Detection from Point Cloud
Weichao Wang, Wendong Mao, Zhongfeng Wang
The deployment of high-accuracy 3D object detection models from point cloud remains a significant challenge due to their substantial computational and memory requirements. To addre…
A Memory-Efficient Framework for Deformable Transformer with Neural Architecture Search
Wendong Mao, Mingfan Zhao, Jianfeng Guan +2
Deformable Attention Transformers (DAT) have shown remarkable performance in computer vision tasks by adaptively focusing on informative image regions. However, their data-dependen…
CDM-QTA: Quantized Training Acceleration for Efficient LoRA Fine-Tuning of Diffusion Model
Jinming Lu, Minghao She, Wendong Mao +1
Fine-tuning large diffusion models for custom applications demands substantial power and time, which poses significant challenges for efficient implementation on mobile devices. In…
An Efficient Sparse Hardware Accelerator for Spike-Driven Transformer
Zhengke Li, Wendong Mao, Siyu Zhang +2
Recently, large models, such as Vision Transformer and BERT, have garnered significant attention due to their exceptional performance. However, their extensive computational requir…
Trio-ViT: Post-Training Quantization and Acceleration for Softmax-Free Efficient Vision Transformer
Huihong Shi, Haikuo Shao, Wendong Mao +1
Motivated by the huge success of Transformers in the field of natural language processing (NLP), Vision Transformers (ViTs) have been rapidly developed and achieved remarkable perf…
An FPGA-Based Reconfigurable Accelerator for Convolution-Transformer Hybrid EfficientViT
Haikuo Shao, Huihong Shi, Wendong Mao +1
Vision Transformers (ViTs) have achieved significant success in computer vision. However, their intensive computations and massive memory footprint challenge ViTs' deployment on em…
A Computationally Efficient Neural Video Compression Accelerator Based on a Sparse CNN-Transformer Hybrid Network
Siyu Zhang, Wendong Mao, Huihong Shi +1
Video compression is widely used in digital television, surveillance systems, and virtual reality. Real-time video decoding is crucial in practical scenarios. Recently, neural vide…
S2R: Exploring a Double-Win Transformer-Based Framework for Ideal and Blind Super-Resolution
Minghao She, Wendong Mao, Huihong Shi +1
Nowadays, deep learning based methods have demonstrated impressive performance on ideal super-resolution (SR) datasets, but most of these methods incur dramatically performance dro…
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…
Multi-scale Convolution Aggregation and Stochastic Feature Reuse for DenseNets
Mingjie Wang, Jun Zhou, Wendong Mao +1
Recently, Convolution Neural Networks (CNNs) obtained huge success in numerous vision tasks. In particular, DenseNets have demonstrated that feature reuse via dense skip connection…
Semi-dense Stereo Matching using Dual CNNs
Wendong Mao, Mingjie Wang, Jun Zhou +1
A robust solution for semi-dense stereo matching is presented. It utilizes two CNN models for computing stereo matching cost and performing confidence-based filtering, respectively…