Zobrazeno 1 - 10
of 15
pro vyhledávání: '"Tianchan Guan"'
Publikováno v:
IEEE Access, Vol 10, Pp 85960-85974 (2022)
Sparse general matrix multiplication (SpGEMM) is an important and expensive computation primitive in many real-world applications. Due to SpGEMM’s inherent irregularity and the vast diversity of its input matrices, developing high-performance SpGEM
Externí odkaz:
https://doaj.org/article/ab734b4fd7d44cfcb673232da5ca0c9c
Publikováno v:
IEEE Access, Vol 10, Pp 79237-79248 (2022)
Sparse general matrix multiplication (SpGEMM) is a fundamental building block for many real-world applications. Since SpGEMM is a well-known memory-bounded application with vast and irregular memory accesses, considering the memory access efficiency
Externí odkaz:
https://doaj.org/article/6327953236ef4340b8d682ee5795e1f8
Autor:
Shuangchen Li, Dimin Niu, Yuhao Wang, Wei Han, Zhe Zhang, Tianchan Guan, Yijin Guan, Heng Liu, Linyong Huang, Zhaoyang Du, Fei Xue, Yuanwei Fang, Hongzhong Zheng, Yuan Xie
Publikováno v:
Proceedings of the 49th Annual International Symposium on Computer Architecture.
Autor:
Xingchen Li, Bingzhe Wu, Guangyu Sun, Zhe Zhang, Zhihang Yuan, Runsheng Wang, Ru Huang, Dimin Niu, Hongzhong Zheng, Zhichao Lu, Liang Zhao, Meng-Fan Marvin Chang, Tianchan Guan, Xin Si
Publikováno v:
2022 IEEE International Symposium on High-Performance Computer Architecture (HPCA).
Autor:
Haozhe Zhu, Bo Jiao, Jinshan Zhang, Xinru Jia, Yunzhengmao Wang, Tianchan Guan, Shengcheng Wang, Dimin Niu, Hongzhong Zheng, Chixiao Chen, Mingyu Wang, Lihua Zhang, Xiaoyang Zeng, Qi Liu, Yuan Xie, Ming Liu
Publikováno v:
2022 IEEE International Solid- State Circuits Conference (ISSCC).
Autor:
Dimin Niu, Shuangchen Li, Yuhao Wang, Wei Han, Zhe Zhang, Yijin Guan, Tianchan Guan, Fei Sun, Fei Xue, Lide Duan, Yuanwei Fang, Hongzhong Zheng, Xiping Jiang, Song Wang, Fengguo Zuo, Yubing Wang, Bing Yu, Qiwei Ren, Yuan Xie
Publikováno v:
2022 IEEE International Solid- State Circuits Conference (ISSCC).
Publikováno v:
IEEE Transactions on Circuits and Systems I: Regular Papers. 66:2593-2605
We present a novel deep learning model for a neural network that reduces both computation and data storage overhead. To do so, the proposed model proposes and combines a binary-weight neural network (BNN) training, a storage reuse technique, and an i
Publikováno v:
IEEE Transactions on Very Large Scale Integration (VLSI) Systems. 27:757-768
Neural associative memory (AM) is one of the critical building blocks for cognitive computing systems. It memorizes (learns) and retrieves input data by information content itself. One of the key challenges of designing AM for intelligent devices is
Publikováno v:
DATE
Neural associative memory (AM) is one of the critical building blocks for cognitive workloads such as classification and recognition. It learns and retrieves memories as humans brain does, i.e., changing the strengths of plastic synapses (weights) ba
Publikováno v:
ISOCC
In this paper we present a new seizure detection system based on neural networks. The system takes raw EEG data without any explicit feature extraction. The removal of explicit feature extraction steps can improve flexibility and also mitigate hardwa