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- research-articleAugust 2023
Characterizing and Understanding Defense Methods for GNNs on GPUs
IEEE Computer Architecture Letters (ICAL), Volume 22, Issue 2July-Dec. 2023, Pages 137–140https://doi.org/10.1109/LCA.2023.3304638Graph neural networks (GNNs) are widely deployed in many vital fields, but suffer from adversarial attacks, which seriously compromise the security in these fields. Plenty of defense methods have been proposed to mitigate the impact of these attacks, ...
- short-paperJune 2023
A High-accurate Multi-objective Ensemble Exploration Framework for Design Space of CPU Microarchitecture
GLSVLSI '23: Proceedings of the Great Lakes Symposium on VLSI 2023June 2023, Pages 379–383https://doi.org/10.1145/3583781.3590280To accelerate the time-consuming multi-objective design space exploration of CPU, previous work trains prediction models using a set of cycle per instruction and power performance metrics derived from a few simulations for sampled design points, then ...
- research-articleFebruary 2023
Simple and efficient heterogeneous graph neural network
AAAI'23/IAAI'23/EAAI'23: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial IntelligenceFebruary 2023, Article No.: 1214, Pages 10816–10824https://doi.org/10.1609/aaai.v37i9.26283Heterogeneous graph neural networks (HGNNs) have the powerful capability to embed rich structural and semantic information of a heterogeneous graph into node representations. Existing HGNNs inherit many mechanisms from graph neural networks (GNNs) ...
- ArticleJanuary 2023
MatGraph: An Energy-Efficient and Flexible CGRA Engine for Matrix-Based Graph Analytics
Algorithms and Architectures for Parallel ProcessingOct 2022, Pages 351–372https://doi.org/10.1007/978-3-031-22677-9_19AbstractGraph analytics is increasingly important for solving problems in various fields. Matrix-based graph analytics has obtained much attention due to its high performance and ease of optimization. In the general architecture, due to the extremely high ...
- ArticleJanuary 2023
- ArticleMarch 2023
GNNSampler: Bridging the Gap Between Sampling Algorithms of GNN and Hardware
Machine Learning and Knowledge Discovery in DatabasesSep 2022, Pages 498–514https://doi.org/10.1007/978-3-031-26419-1_30AbstractSampling is a critical operation in Graph Neural Network (GNN) training that helps reduce the cost. Previous literature has explored improving sampling algorithms via mathematical and statistical methods. However, there is a gap between sampling ...
- research-articleAugust 2022
Alleviating datapath conflicts and design centralization in graph analytics acceleration
DAC '22: Proceedings of the 59th ACM/IEEE Design Automation ConferenceJuly 2022, Pages 901–906https://doi.org/10.1145/3489517.3530524Previous graph analytics accelerators have achieved great improvement on throughput by alleviating irregular off-chip memory accesses. However, on-chip side datapath conflicts and design centralization have become the critical issues hindering further ...
- research-articleJuly 2022
Characterizing and Understanding HGNNs on GPUs
IEEE Computer Architecture Letters (ICAL), Volume 21, Issue 2July-Dec. 2022, Pages 69–72https://doi.org/10.1109/LCA.2022.3198281Heterogeneous graph neural networks (HGNNs) deliver powerful capacity in heterogeneous graph representation learning. The execution of HGNNs is usually accelerated by GPUs. Therefore, characterizing and understanding the execution pattern of HGNNs on GPUs ...
- short-paperJune 2022
HetGraph: A High Performance CPU-CGRA Architecture for Matrix-based Graph Analytics
GLSVLSI '22: Proceedings of the Great Lakes Symposium on VLSI 2022June 2022, Pages 387–391https://doi.org/10.1145/3526241.3530382In this paper, we explore graph analytics on a heterogeneous platform named HetGraph integrating with CPU and a flexible CGRA accelerator called RFU for matrix-based paradigm in this paper. RFU utilizes the lightweight pipeline without data hazards to ...
- research-articleJanuary 2022
Characterizing and Understanding Distributed GNN Training on GPUs
IEEE Computer Architecture Letters (ICAL), Volume 21, Issue 1Jan.-June 2022, Pages 21–24https://doi.org/10.1109/LCA.2022.3168067Graph neural network (GNN) has been demonstrated to be a powerful model in many domains for its effectiveness in learning over graphs. To scale GNN training for large graphs, a widely adopted approach is distributed training which accelerates training ...
- research-articleDecember 2020
fuseGNN: accelerating graph convolutional neural network training on GPGPU
ICCAD '20: Proceedings of the 39th International Conference on Computer-Aided DesignNovember 2020, Article No.: 60, Pages 1–9https://doi.org/10.1145/3400302.3415610Graph convolutional neural networks (GNN) have achieved state-of-the-art performance on tasks like node classification. It has become a new workload family member in data-centers. GNN works on irregular graph-structured data with three distinct phases: ...
- research-articleOctober 2019
Alleviating Irregularity in Graph Analytics Acceleration: a Hardware/Software Co-Design Approach
- Mingyu Yan,
- Xing Hu,
- Shuangchen Li,
- Abanti Basak,
- Han Li,
- Xin Ma,
- Itir Akgun,
- Yujing Feng,
- Peng Gu,
- Lei Deng,
- Xiaochun Ye,
- Zhimin Zhang,
- Dongrui Fan,
- Yuan Xie
MICRO '52: Proceedings of the 52nd Annual IEEE/ACM International Symposium on MicroarchitectureOctober 2019, Pages 615–628https://doi.org/10.1145/3352460.3358318Graph analytics is an emerging application which extracts insights by processing large volumes of highly connected data, namely graphs. The parallel processing of graphs has been exploited at the algorithm level, which in turn incurs three ...
- research-articleSeptember 2015
Hybrid facial image feature extraction and recognition for non-invasive chronic fatigue syndrome diagnosis
Computers in Biology and Medicine (CBIM), Volume 64, Issue CSeptember 2015, Pages 30–39https://doi.org/10.1016/j.compbiomed.2015.06.005Due to an absence of reliable biochemical markers, the diagnosis of chronic fatigue syndrome (CFS) mainly relies on the clinical symptoms, and the experience and skill of the doctors currently. To improve objectivity and reduce work intensity, a hybrid ...
- articleOctober 2014
Fatigue detection with 3D facial features based on binocular stereo vision
Integrated Computer-Aided Engineering (ICAE), Volume 21, Issue 4October 2014, Pages 387–397https://doi.org/10.3233/ICA-140476Fatigue may lead to potential accidents, but its diagnosis is difficult so it is easy to be delayed or missed. In this paper, a novel 3D facial-image-based fatigue detection method is presented. There are three steps involved: First, 3D surface ...