中国科学院机构知识库网格系统: 检索

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	CoAxNN: Optimizing on-device deep learning with conditional approximate neural networks 期刊论文 OAI收割 JOURNAL OF SYSTEMS ARCHITECTURE, 2023, 卷号: 143, 页码: 14 作者: Li, Guangli; Ma, Xiu; Yu, Qiuchu; Liu, Lei; Liu, Huaxiao \| 收藏 \| 浏览/下载：18/0 \| 提交时间：2023/12/04 On-device deep learning Efficient neural networks Model approximation and optimization
	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：41/0 \| 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：58/0 \| 提交时间：2021/11/03 Efficient training graph convolutional networks (GCNs) graph neural networks (GNNs) sampling method
	ECBC: Efficient Convolution via Blocked Columnizing 期刊论文 OAI收割 IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2021, 2021, 页码: 13, 13 作者: Zhao, Tianli; Hu, Qinghao; He, Xiangyu; Xu, Weixiang; Wang, Jiaxing \| 收藏 \| 浏览/下载：42/0 \| 提交时间：2022/01/27 Convolution Convolution Tensors Layout Memory management Indexes Transforms Performance evaluation Convolutional neural networks (CNNs) direct convolution high performance computing for mobile devices im2col convolution memory-efficient convolution (MEC) Tensors Layout Memory management Indexes Transforms Performance evaluation Convolutional neural networks (CNNs) direct convolution high performance computing for mobile devices im2col convolution memory-efficient convolution (MEC)
	A Quantitative Exploration of Collaborative Pruning and Approximation Computing Towards Energy Efficient Neural Networks 期刊论文 OAI收割 IEEE DESIGN & TEST, 2020, 卷号: 37, 期号: 1, 页码: 36-45 作者: He, Xin; Yan, Guihai; Lu, Wenyan; Zhang, Xuan; Liu, Ke \| 收藏 \| 浏览/下载：32/0 \| 提交时间：2020/12/10 Resilience Energy consumption Approximate computing Collaboration Computational modeling Artificial neural networks Optimization Neural network Energy efficient computing Network pruning Approximate computing
	Efficient coding matters in the organization of the early visual system 期刊论文 OAI收割 NEURAL NETWORKS, 2018, 卷号: 105, 页码: 218-226 作者: Kong, Qingqun; Han, Jiuqi; Zeng, Yi; Xu, Bo \| 收藏 \| 浏览/下载：38/0 \| 提交时间：2018/10/10 Early Visual Stages Hierarchical Structure Efficient Coding Brain-inspired Neural Networks
	Lossless wavelet compression on medical image (EI CONFERENCE) 会议论文 OAI收割 4th International Conference on Photonics and Imaging in Biology and Medicine, September 3, 2005 - September 6, 2005, Tianjin, China 作者: Liu H.; Liu H.; Liu H. 收藏 \| 浏览/下载：37/0 \| 提交时间：2013/03/25 An increasing number of medical imagery is created directly in digital form. Such as Clinical image Archiving and Communication Systems (PACS). as well as telemedicine networks require the storage and transmission of this huge amount of medical image data. Efficient compression of these data is crucial. Several lossless and lossy techniques for the compression of the data have been proposed. Lossless techniques allow exact reconstruction of the original imagery while lossy techniques aim to achieve high compression ratios by allowing some acceptable degradation in the image. Lossless compression does not degrade the image thus facilitating accurate diagnosis of course at the expense of higher bit rates i.e. lower compression ratios. Various methods both for lossy (irreversible) and lossless (reversible) image compression are proposed in the literature. The recent advances in the lossy compression techniques include different methods such as vector quantization wavelet coding neural networks and fractal coding. Although these methods can achieve high compression ratios (of the order 50:1 or even more) they do not allow reconstructing exactly the original version of the input data. Lossless compression techniques permit the perfect reconstruction of the original image but the achievable compression ratios are only of the order 2:1 up to 4:1. In our paper we use a kind of lifting scheme to generate truly loss-less non-linear integer-to-integer wavelet transforms. At the same time we exploit the coding algorithm producing an embedded code has the property that the bits in the bit stream are generated in order of importance so that all the low rate codes are included at the beginning of the bit stream. Typically the encoding process stops when the target bit rate is met. Similarly the decoder can interrupt the decoding process at any point in the bil stream and still reconstruct the image. Therefore a compression scheme generating an embedded code can start sending over the network the coarser version of the image first and continues with the progressive transmission of the refinement details. Experimental results show that our method can get a perfect performance in compression ratio and reconstructive image.