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检索条件"主题词=Computation Graph"
14 条 记 录,以下是1-10 订阅
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GPABE: GPU-Based Parallelization Framework for Attribute-Based Encryption Schemes
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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2025年 第3期36卷 520-536页
作者: Xu, Wenhan Ma, Hui Zhang, Rui Li, Jianhao Chinese Acad Sci Inst Informat Engn IIE State Key Lab Cyberspace Secur Def Beijing 100093 Peoples R China Univ Chinese Acad Sci Sch Cybersecur Beijing 100049 Peoples R China
Attribute-based encryption (ABE) has emerged as a new paradigm for access control in cloud computing. However, despite the many promising features of ABE, its deployment in real-world systems is still limited, partial... 详细信息
来源: 评论
Edgeless-GNN: Unsupervised Representation Learning for Edgeless Nodes
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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING 2024年 第1期12卷 150-162页
作者: Shin, Yong-Min Tran, Cong Shin, Won-Yong Cao, Xin Yonsei Univ Sch Math & Comp Computat Sci & Engn Seoul 03722 South Korea Yonsei Univ Machine Intelligence & Data Sci Lab Seoul 03722 South Korea Posts & Telecommun Inst Technol Fac Informat Technol Hanoi 100000 Vietnam Univ New South Wales Sch Comp Sci & Engn Sydney 2052 Australia
We study the problem of embedding edgeless nodes such as users who newly enter the underlying network, while using graph neural networks (GNNs) widely studied for effective representation learning of graphs. Our study... 详细信息
来源: 评论
A high-performance dataflow-centric optimization framework for deep learning inference on the edge
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JOURNAL OF SYSTEMS ARCHITECTURE 2024年 152卷
作者: Zhang, Runhua Jiang, Hongxu Geng, Jinkun Tian, Fangzheng Ma, Yuhang Wang, Haojie Beihang Univ Beijing Peoples R China Stanford Univ Stanford CA USA Tsinghua Univ Beijing Peoples R China
Edge computing has been emerging as a popular scenario for model inference. However, the inference performance on edge devices (e.g., Multi-Core DSP, FGPA, etc.) suffers from inefficiency due to the lack of highly opt... 详细信息
来源: 评论
DLGR: A Rule-Based Approach to graph Replacement for Deep Learning  26
DLGR: A Rule-Based Approach to Graph Replacement for Deep Le...
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26th International Conference on Engineering of Complex Computer Systems (ICECCS)
作者: Ma, Enze Beijing Forestry Univ Dept Comp Sci Beijing Peoples R China
In deep learning libraries like TensorFlow, computations are manually batched as computation graphs. graph replacement is then an optimization that replaces one subgraph of a computation graph with another whilst keep... 详细信息
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GLite: A Fast and Efficient Automatic graph-Level Optimizer for Large-Scale DNNs  22
GLite: A Fast and Efficient Automatic Graph-Level Optimizer ...
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59th ACM/IEEE Design Automation Conference (DAC) - From Chips to Systems - Learn Today, Create Tomorrow
作者: Li, Jiaqi Peng, Min Li, Qingan Peng, Meizheng Yuan, Mengting Wuhan Univ Wuhan Peoples R China
We propose a scalable graph-level optimizer named GLite to speed up search-based optimizations on large neural networks. GLite leverages a potential-based partitioning strategy to partition large computation graphs in... 详细信息
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Enabling Resource-Aware Mapping of Spiking Neural Networks via Spatial Decomposition
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IEEE EMBEDDED SYSTEMS LETTERS 2021年 第3期13卷 142-145页
作者: Balaji, Adarsha Song, Shihao Das, Anup Krichmar, Jeffrey Dutt, Nikil Shackleford, James Kandasamy, Nagarajan Catthoor, Francky Drexel Univ Dept Elect & Comp Engn Philadelphia PA 19104 USA Univ Calif Irvine Dept Comp Sci Irvine CA 92697 USA IMEC B-3001 Leuven Belgium Katholieke Univ Leuven ESAT B-3000 Leuven Belgium
With growing model complexity, mapping spiking neural network (SNN)-based applications to tile-based neuromorphic hardware is becoming increasingly challenging. This is because the synaptic storage resources on a tile... 详细信息
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HARP: Holistic Analysis for Refactoring Python-Based Analytics Programs  20
HARP: Holistic Analysis for Refactoring Python-Based Analyti...
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42nd ACM/IEEE International Conference on Software Engineering - Companion Proceedings (ICSE-Companion) / 42nd ACM/IEEE International Conference on Software Engineering - Software Engineering in Practice (ICSE-SEIP)
作者: Zhou, Weijie Zhao, Yue Zhang, Guoqiang Shen, Xipeng North Carolina State Univ Raleigh NC 27695 USA Facebook Menlo Pk CA USA
Modern machine learning programs are often written in Python, with the main computations specified through calls to some highly optimized libraries (e.g., TensorFlow, PyTorch). How to maximize the computing efficiency... 详细信息
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Compiling Spiking Neural Networks to Mitigate Neuromorphic Hardware Constraints  11
Compiling Spiking Neural Networks to Mitigate Neuromorphic H...
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11th International Green and Sustainable Computing Workshop (IGSC)
作者: Balaji, Adarsha Das, Anup Drexel Univ Dept Elect & Comp Engn Philadelphia PA 19104 USA
Spiking Neural Networks (SNNs) are efficient computation models to perform spatio-temporal pattern recognition on resource- and power-constrained platforms. SNNs executed on neuromorphic hardware can further reduce en... 详细信息
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Model-checking task-parallel programs for data-race
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INNOVATIONS IN SYSTEMS AND SOFTWARE ENGINEERING 2019年 第3-4期15卷 289-306页
作者: Nakade, Radha Mercer, Eric Aldous, Peter Storey, Kyle Ogles, Benjamin Hooker, Joshua Powell, Sheridan Jacob McCarthy, Jay Brigham Young Univ Provo UT 84602 USA Univ Massachusetts Lowell MA USA
Many of the correctness properties afforded by task-parallel programming models such as OpenMP, Cilk, X10, Chapel, Habanero, etc. rely on data-race freedom. The research in this paper studies data-race in the context ... 详细信息
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An Incremental Approach to Scope-Bounded Checking Using a Lightweight Formal Method
An Incremental Approach to Scope-Bounded Checking Using a Li...
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2nd World Congress on Formal Methods/16th International Symposium on Formal Methods (FM 2009)
作者: Shao, Danhua Khurshid, Sarfraz Perry, Dewayne E. Univ Texas Austin Dept Elect & Comp Engn Austin TX 78712 USA
We present a novel approach to optimize scope-bounded checking programs using a relational constraint solver. Given a program and its correctness specification, the traditional approach translates a bounded code segme... 详细信息
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