机构

• 计算技术研究所 [20]
• 自动化研究所 [2]
• 半导体研究所 [1]

采集方式

• OAI收割 [23]

内容类型

• 期刊论文 [23]

发表日期

• 2024 [1]
• 2023 [2]
• 2022 [8]
• 2021 [5]
• 2020 [2]
• 2019 [1]
• 更多

学科主题

筛选

浏览/检索结果: 共23条，第1-10条 帮助 条数/页： 5101520253035404550556065707580859095100 排序方式： 请选择提交时间升序提交时间降序题名升序题名降序作者升序作者降序发表日期升序发表日期降序 Improving Utilization of Dataflow Unit for Multi-Batch Processing 期刊论文  OAI收割 ACM TRANSACTIONS ON ARCHITECTURE AND CODE OPTIMIZATION, 2024, 卷号: 21, 期号: 1, 页码: 26 作者:  Fan, Zhihua;  Li, Wenming;  Wang, Zhen;  Yang, Yu;  Ye, Xiaochun   |  收藏  |  浏览/下载：0/0  |  提交时间：2024/05/20 Utilization  network-on-chip  decoupled architecture  batch processing   Accelerating Convolutional Neural Networks by Exploiting the Sparsity of Output Activation 期刊论文  OAI收割 IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, 2023, 卷号: 34, 期号: 12, 页码: 3253-3265 作者:  Fan, Zhihua;  Li, Wenming;  Wang, Zhen;  Liu, Tianyu;  Wu, Haibin   |  收藏  |  浏览/下载：1/0  |  提交时间：2024/05/20 Accelerator  output activation  prediction  sparse convolutional neural network   Characterizing and Understanding Defense Methods for GNNs on GPUs 期刊论文  OAI收割 IEEE COMPUTER ARCHITECTURE LETTERS, 2023, 卷号: 22, 期号: 2, 页码: 137-140 作者:  Wu, Meng;  Yan, Mingyu;  Yang, Xiaocheng;  Li, Wenming;  Zhang, Zhimin   |  收藏  |  浏览/下载：6/0  |  提交时间：2023/12/04 Kernel  Purification  Estimation  Graphics processing units  Perturbation methods  Electric breakdown  Training  Graph neural networks  defense  execution semantic  execution pattern  overhead   JBNN: A Hardware Design for Binarized Neural Networks Using Single-Flux-Quantum Circuits 期刊论文  OAI收割 IEEE TRANSACTIONS ON COMPUTERS, 2022, 卷号: 71, 期号: 12, 页码: 3203-3214 作者:  Fu, Rongliang;  Huang, Junying;  Wu, Haibin;  Ye, Xiaochun;  Fan, Dongrui   |  收藏  |  浏览/下载：9/0  |  提交时间：2023/07/12 Superconducting  single-flux-quantum  accelerator  binarized neural network   Multi-Node Acceleration for Large-Scale GCNs 期刊论文  OAI收割 IEEE TRANSACTIONS ON COMPUTERS, 2022, 卷号: 71, 期号: 12, 页码: 3140-3152 作者:  Sun, Gongjian;  Yan, Mingyu;  Wang, Duo;  Li, Han;  Li, Wenming   |  收藏  |  浏览/下载：13/0  |  提交时间：2023/07/12 Deep learning  graph neural network  hardware accelerator  multi-node system  communication optimization   General spiking neural network framework for the learning trajectory from a noisy mmWave radar 期刊论文  OAI收割 Neuromorphic Computing and Engineering, 2022, 卷号: 2, 期号: 3 作者:  Liu,Xin;  Yan,Mingyu;  Deng,Lei;  Wu,Yujie;  Han,De   |  收藏  |  浏览/下载：6/0  |  提交时间：2023/05/31 millimeter wave radar  spiking neural networks  attention mechanism  model robustness  trajectory estimation   A synergistic reinforcement learning-based framework design in driving automation 期刊论文  OAI收割 COMPUTERS & ELECTRICAL ENGINEERING, 2022, 卷号: 101, 页码: 15 作者:  Qi, Yuqiong;  Hu, Yang;  Wu, Haibin;  Li, Shen;  Ye, Xiaochun   |  收藏  |  浏览/下载：27/0  |  提交时间：2022/12/07 Autonomous Driving  Heterogeneous Multicore AI Accelerator  Criteria  Reinforcement Learning  Scheduling   Characterizing and Understanding HGNNs on GPUs 期刊论文  OAI收割 IEEE COMPUTER ARCHITECTURE LETTERS, 2022, 卷号: 21, 期号: 2, 页码: 69-72 作者:  Yan, Mingyu;  Zou, Mo;  Yang, Xiaocheng;  Li, Wenming;  Ye, Xiaochun   |  收藏  |  浏览/下载：21/0  |  提交时间：2022/12/07 Kernel  Semantics  Aggregates  Mercury (metals)  Motion pictures  Graphics processing units  Electric breakdown  Heterogeneous graph neural networks  GNNs  characterization  execution semantic  execution pattern   Sampling Methods for Efficient Training of Graph Convolutional Networks: A Survey 期刊论文  OAI收割 IEEE-CAA JOURNAL OF AUTOMATICA SINICA, 2022, 卷号: 9, 期号: 2, 页码: 205-234 作者:  Liu, Xin;  Yan, Mingyu;  Deng, Lei;  Li, Guoqi;  Ye, Xiaochun   |  收藏  |  浏览/下载：25/0  |  提交时间：2022/06/21 Efficient training  graph convolutional networks (GCNs)  graph neural networks (GNNs)  sampling method   Sampling Methods for Efficient Training of Graph Convolutional Networks: A Survey 期刊论文  OAI收割 IEEE/CAA Journal of Automatica Sinica, 2022, 卷号: 9, 期号: 2, 页码: 205-234 作者:  Xin Liu;  Mingyu Yan;  Lei Deng;  Guoqi Li;  Xiaochun Ye   |  收藏  |  浏览/下载：47/0  |  提交时间：2021/11/03 Efficient training  graph convolutional networks (GCNs)  graph neural networks (GNNs)  sampling method