The following publications are possibly variants of this publication:
- A Low-Power Deconvolutional Accelerator for Convolutional Neural Network Based Segmentation on FPGA: Abstract OnlyShuanglong Liu, Xinyu Niu, Wayne Luk. fpga 2018: 293 [doi]
- Software-Defined FPGA-Based Accelerator for Deep Convolutional Neural Networks: (Abstract Only)Yankang Du, Qinrang Liu, Shuai Wei, Chen Gao. fpga 2018: 291 [doi]
- A Self-adaptation Method of Fitting Convolutional Neural Network into FPGA: Abstract Only)Ning Mao, Zhihong Huang, Xing Wei, He Zhao, Xinkai Di, Le Yu, Haigang Yang. fpga 2018: 295 [doi]
- Learning Convolutional Neural Networks for Data-Flow Graph Mapping on Spatial Programmable Architectures (Abstract Only)Shouyi Yin, Dajiang Liu, Lifeng Sun, Xinhan Lin, Leibo Liu, Shaojun Wei. fpga 2017: 295 [doi]
- Domino: An Asynchronous and Energy-efficient Accelerator for Graph Processing: (Abstract Only)Chongchong Xu, Chao Wang, YiWei Zhang, Lei Gong, Xi Li, Xuehai Zhou. fpga 2018: 289 [doi]
- A FPGA Friendly Approximate Computing Framework with Hybrid Neural Networks: (Abstract Only)Haiyue Song, Xiang Song, Tianjian Li, Hao Dong, Naifeng Jing, Xiaoyao Liang, Li Jiang. fpga 2018: 286 [doi]