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20192022
most citedAn Efficient Hardware Accelerator for Structured Sparse Convolutional Neural Networks on FPGAs

8 citations · 11 across the 7 of their papers we have counts for

collaborators

8 papers

cs.CV2022

Thermal Infrared Image Inpainting via Edge-Aware Guidance

Zeyu Wang, Haibin Shen, Changyou Men +2

Image inpainting has achieved fundamental advances with deep learning. However, almost all existing inpainting methods aim to process natural images, while few target Thermal Infra…

cs.AR2021

A Low Power In-Memory Multiplication andAccumulation Array with Modified Radix-4 Inputand Canonical Signed Digit Weights

Rui Xiao, Kejie Huang, Yewei Zhang +1

A mass of data transfer between the processing and storage units has been the leading bottleneck in modern Von-Neuman computing systems, especially when used for Artificial Intelli…

cs.CV2020

C2F-FWN: Coarse-to-Fine Flow Warping Network for Spatial-Temporal Consistent Motion Transfer

Dongxu Wei, Xiaowei Xu, Haibin Shen +1

Human video motion transfer (HVMT) aims to synthesize videos that one person imitates other persons' actions. Although existing GAN-based HVMT methods have achieved great success,…

cs.AR2020

An 8-bit In Resistive Memory Computing Core with Regulated Passive Neuron and Bit Line Weight Mapping

Yewei Zhang, Kejie Huang, Rui Xiao +1

The rapid development of Artificial Intelligence (AI) and Internet of Things (IoT) increases the requirement for edge computing with low power and relatively high processing speed…

eess.IV2020

A GAN-based Tunable Image Compression System

Lirong Wu, Kejie Huang, Haibin Shen

The method of importance map has been widely adopted in DNN-based lossy image compression to achieve bit allocation according to the importance of image contents. However, insuffic…

eess.SY20208 cited

An Efficient Hardware Accelerator for Structured Sparse Convolutional Neural Networks on FPGAs

Chaoyang Zhu, Kejie Huang, Shuyuan Yang +3

Deep Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance in a wide range of applications. However, deeper CNN models, which are usually computation cons…