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IEEE Conference on computer Communications

作者： Lu Tang Qun Huang Patrick P. C. Lee Department of Computer Science and Engineering The Chinese University of Hong Kong State Key Lab of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences

ISBN: (纸本)9781728105161

Fast detection of heavy flows (e.g., heavy hitters and heavy changers) in massive network traffic is challenging due to the stringent requirements of fast packet processing and limited resource availability. Invertible sketches are summary data structures that can recover heavy flows with small memory footprints and bounded errors, yet existing invertible sketches incur high memory access overhead that leads to performance degradation. We present MV-Sketch, a fast and compact invertible sketch that supports heavy flow detection with small and static memory allocation. MV-Sketch tracks candidate heavy flows inside the sketch data structure via the idea of majority voting, such that it incurs small memory access overhead in both update and query operations, while achieving high detection accuracy. We present theoretical analysis on the memory usage, performance, and accuracy of MV-Sketch. Trace-driven evaluation shows that MVSketch achieves higher accuracy than existing invertible sketches, with up to 3.38× throughput gain. We also show how to boost the performance of MV-Sketch with SIMD instructions.

关键词： Sketches Reversibility Heavy Packet Processing traffic network degradation of performance Fasting

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A Markov Traffic Model for Signalized Traffic Networks Based on Bayesian Estimation

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IFAC-PapersOnLine 2020年第2期53卷 15029-15034页

作者： S.Y. Liu S. Lin Y.B. Wang B. De Schutter W.H.K. Lam School of Computer Science and Technology University of Chinese Academy of Sciences Beijing 100089 China Key Lab of Big Data Mining and Knowledge Management Chinese Academy of Sciences College of Civil Engineering and Architecture Zhejiang University PR China Delft Center for Systems and Control Delft University of Technology The Netherlands Department of Civil and Structural Engineering The Hong Kong Polytechnic University Hong Kong SAR China

In order to better understand the stochastic dynamic features of signalized traffic networks, we propose a Markov traffic model to simulate the dynamics of traffic link flow density for signalized urban traffic networks with demand uncertainty. In this model, we have four different state modes for the link according to different congestion levels of the link. Each link can only be in one of the four link state modes at any time, and the transition probability from one state to the other state is estimated by Bayesian estimation based on the distributions of the dynamic traffic flow densities, and the posterior probabilities. Therefore, we use a first-order Markov Chain Model to describe the dynamics of the traffic flow evolution process. We illustrate our approach for a small traffic network. Compared with the data from the microscopic traffic simulator SUMO, the proposed model can estimate the link traffic densities accurately and can give a reliable estimation of the uncertainties in the dynamic process of signalized traffic networks.

关键词： Markov traffic model Traffic signals Bayesian Urban traffic network

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Optimization Space Exploration of Hardware Design for CRYSTALS-KYBER

Optimization Space Exploration of Hardware Design for CRYSTA...

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Asian Test Symposium (ATS)

作者： Yixuan Zhao Zhiteng Chao Jing Ye Wen Wang Yuan Cao Shuai Chen Xiaowei Li Huawei Li State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China Yale University US Hohai University China Rock Solid Security Lab FiberHome Co. Ltd. China

ISBN: (数字)9781728174679

ISBN: (纸本)9781728174686

Public key cryptography is important in the global communication digital infrastructure. However, the emergence of quantum computer and Shor algorithm has greatly threatened the security of public key cryptography. The CRYSTALS-KYBER, as a lattice-based KEM algorithm, passed three rounds of a global solicitation for post-quantum cryptography algorithms held by the National Institute of Standards and technology (NIST). This paper explores the implementation and optimization space of hardware design according to CRYSTALS-KYBER algorithm. We analyze its software code and try different strategies to optimize the hardware implementation, and conduct comparative analysis in terms of area and speed. The experimental results show that the performance can be greatly improved by moderately optimizing the loops. In comparison with optimal results of the work [12], our optimizations improve the performance by up to 74.6% for encapsulation algorithm and 54.4% for decapsulation algorithm.

关键词： Cryptography Optimization Hardware Pipeline processing Encapsulation computers Space exploration

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Effective fusion of deep multitasking representations for robust visual tracking

arXiv

引用

arXiv 2020年

作者： Marvasti-Zadeh, Seyed Mojtaba Ghanei-Yakhdan, Hossein Kasaei, Shohreh Nasrollahi, Kamal Moeslund, Thomas B. Department of Electrical Engineering Yazd University Yazd Iran Sharif University of Technology Tehran Iran Vision and Learning Lab. University of Alberta Edmonton Canada Department of Electrical Engineering Yazd University Yazd Iran Department of Computer Engineering Sharif University of Technology Tehran Iran Department of Architecture Design and Media Technology Aalborg University Aalborg Denmark

Visual object tracking remains an active research field in computer vision due to persisting challenges with various problem-specific factors in real-world scenes. Many existing tracking methods based on discriminative correlation filters (DCFs) employ feature extraction networks (FENs) to model the target appearance during the learning process. However, using deep feature maps extracted from FENs based on different residual neural networks (ResNets) has not previously been investigated. This paper aims to evaluate the performance of twelve state-of-the-art ResNet-based FENs in a DCF-based framework to determine the best for visual tracking purposes. First, it ranks their best feature maps and explores the generalized adoption of the best ResNet-based FEN into another DCF-based method. Then, the proposed method extracts deep semantic information from a fully convolutional FEN and fuses it with the best ResNet-based feature maps to strengthen the target representation in the learning process of continuous convolution filters. Finally, it introduces a new and efficient semantic weighting method (using semantic segmentation feature maps on each video frame) to reduce the drift problem. Extensive experimental results on the well-known OTB-2013, OTB-2015, TC-128, and VOT-2018 visual tracking datasets demonstrate that the proposed method effectively outperforms state-of-the-art methods in terms of precision and robustness of visual tracking. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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Edge AIBench: Towards Comprehensive End-to-End Edge Computing Benchmarking 1st

Edge AIBench: Towards Comprehensive End-to-End Edge Computin...

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1st International Symposium on Benchmarking, Measuring, and Optimization, Bench 2018

作者： Hao, Tianshu Huang, Yunyou Wen, Xu Gao, Wanling Zhang, Fan Zheng, Chen Wang, Lei Ye, Hainan Hwang, Kai Ren, Zujie Zhan, Jianfeng State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Dover DE United States Beijing Academy of Frontier Sciences and Technology Beijing China Chinese University of Hongkong at Shenzhen Shenzhen China Zhejiang Lab Zhejiang China

ISBN: (纸本)9783030328122

In edge computing scenarios, the distribution of data and collaboration of workloads on different layers are serious concerns for performance, privacy, and security issues. So for edge computing benchmarking, we must take an end-to-end view, considering all three layers: client-side devices, edge computing layer, and cloud servers. Unfortunately, the previous work ignores this most important point. This paper presents the BenchCouncil’s coordinated effort on edge AI benchmarks, named Edge AIBench. In total, Edge AIBench models four typical application scenarios: ICU Patient Monitor, Surveillance Camera, Smart Home, and Autonomous Vehicle with the focus on data distribution and workload collaboration on three layers. Edge AIBench is publicly available from http://***/EdgeAIBench/***. We also build an edge computing testbed with a federated learning framework to resolve performance, privacy, and security issues. © 2019, Springer Nature Switzerland AG.

关键词： Edge computing

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BTI Aging Monitoring based on SRAM Start-up Behavior

BTI Aging Monitoring based on SRAM Start-up Behavior

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Asian Test Symposium (ATS)

作者： Shengyu Duan Peng Wang Gaole Sai School of Computer Engineering and Science Shanghai University Shanghai China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology China

ISBN: (数字)9781728174679

ISBN: (纸本)9781728174686

Bias Temperature Instability (BTI) is one of the dominant CMOS aging mechanisms. It causes time-dependent variation, threatening circuit lifetime reliability. BTI-induced circuit errors are not detectable at the fabrication stage. On-line monitoring schemes are therefore necessary to capture the degradations during the operational time. Traditional aging monitoring techniques exhibit high implementation complexity and low stability. In this paper, we propose a BTI monitoring approach by simply tracking the start-up behavior of SRAM cells. SRAM is a widely used on-chip device in many applications. We study the impact of BTI for SRAM start-up values and age some cells in a manipulated manner. The BTI degradation is evaluated based on the number of SRAM cells starting with a certain value. This technique can be used to estimate the degradation for on-chip logic circuits without introducing additional circuitry, and thus has very low implementation complexity. We use an SRAM array with 1024 cells to estimate the degradations for multiple logic circuits, and show the average mean absolute percentage error as 8.48%. In addition, this technique is robust considering process, voltage and temperature variations.

关键词： Degradation Monitoring Logic circuits Transistors SRAM cells Aging System-on-chip

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A Fast and Compact Invertible Sketch for Network-Wide Heavy Flow Detection

arXiv

引用

arXiv 2019年

作者： Tang, Lu Huang, Qun Lee, Patrick P.C. Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong State Key Lab of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences

Fast detection of heavy flows (e.g., heavy hitters and heavy changers) in massive network traffic is challenging due to the stringent requirements of fast packet processing and limited resource availability. Invertible sketches are summary data structures that can recover heavy flows with small memory footprints and bounded errors, yet existing invertible sketches incur high memory access overhead that leads to performance degradation. We present MV-Sketch, a fast and compact invertible sketch that supports heavy flow detection with small and static memory allocation. MV-Sketch tracks candidate heavy flows inside the sketch data structure via the idea of majority voting, such that it incurs small memory access overhead in both update and query operations, while achieving high detection accuracy. We present theoretical analysis on the memory usage, performance, and accuracy of MV-Sketch in both local and network-wide scenarios. We further show how MV-Sketch can be implemented and deployed on P4-based programmable switches subject to hardware deployment constraints. We conduct evaluation in both software and hardware environments. Trace-driven evaluation in software shows that MV-Sketch achieves higher accuracy than existing invertible sketches, with up to 3.38× throughput gain. We also show how to boost the performance of MV-Sketch with SIMD instructions. Furthermore, we evaluate MV-Sketch on a Barefoot Tofino switch and show how MV-Sketch achieves line-rate measurement with limited hardware resource overhead. Copyright © 2019, The Authors. All rights reserved.

关键词： Memory architecture

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Decoding surface code with a distributed neural network based decoder

arXiv

引用

arXiv 2019年

作者： Varsamopoulos, Savvas Bertels, Koen Almudever, Carmen G. Quantum Computer Architecture Lab Delft University of Technology QuTech Delft University of Technology P.O. Box 5046 Delft2600 GA Netherlands

There has been a rise in decoding quantum error correction codes with neural network based decoders, due to the good decoding performance achieved and adaptability to any noise model. However, the main challenge is scalability to larger code distances due to an exponential increase of the error syndrome space. Note that, successfully decoding the surface code under realistic noise assumptions will limit the size of the code to less than 100 qubits with current neural network based decoders. Such a problem can be tackled by a distributed way of decoding, similar to the Renormalization Group (RG) decoders. In this paper, we introduce a decoding algorithm that combines the concept of RG decoding and neural network based decoders. We tested the decoding performance under depolarizing noise with noiseless error syndrome measurements for the rotated surface code and compared against the Blossom algorithm and a neural network based decoder. We show that similar level of decoding performance can be achieved between all tested decoders while providing a solution to the scalability issues of neural network based decoders. Copyright © 2019, The Authors. All rights reserved.

关键词： Decoding

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Towards advanced interfaces for citizen curation

Towards advanced interfaces for citizen curation

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2020 AVI2CH Workshop on Advanced Visual Interfaces and Interactions in Cultural Heritage, AVI2CH 2020

作者： Bruni, Luis Emilio Diaz, Lily Gangemi, Aldo Daga, Enrico Kuflik, Tsvi Mulholland, Paul Pescarin, Sofia Damiano, Rossana Lieto, Antonio Peroni, Silivio Wecker, Alan Department of Architecture Design and Media Technology Alborg University Denmark Department of Media Media lab Aalto University Aalto Finland DHARC - Department of Classical Philology and Italian Studies University of Bologna ISTC-CNR Rome Italy Knowledge Media Institute Open University Milton Keynes United Kingdom Information Systems University of Haifa Haifa Israel CNR ISPC Rome Italy Computer Science and CIRMA University of Turin Turin Italy DHARC - Department of Classical Philology and Italian Studies University of Bologna Rome Italy

The SPICE project builds on the growing trend for museums, rather than providing an authoritative view, to present multiple voices related to their collection and exhibitions. In SPICE, an approach we term citizen curation is proposed as a way of supporting visitors to share their own interpretations of museum objects and reflect on the variety of interpretations contributed by others. In order to capture a wide range of voices, interfaces will be designed specifically to engage minority groups that tend to be under-represented in cultural activities. To achieve this goal, the interface will need to be intuitive, aesthetic and accessible for different audiences. The paper presents the challenges we face and initial proposals for engaging visitors in citizen curation. © Copyright 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Museums

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Spatiotemporal Attention Autoencoder (STAAE) for ADHD Classification 23rd

Spatiotemporal Attention Autoencoder (STAAE) for ADHD Classi...

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23rd International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2020

作者： Dong, Qinglin Qiang, Ning Lv, Jinglei Li, Xiang Liu, Tianming Li, Quanzheng Center for Advanced Medical Computing and Analysis Department of Radiology Massachusetts General Hospital and Harvard Medical School BostonMA United States School of Physics and Information Technology Shaanxi Normal University Xian China School of Biomedical Engineering and Sydney Imaging Brain and Mind Centre The University of Sydney Camperdown Australia Cortical Architecture Imaging and Discovery Lab Department of Computer Science and Bioimaging Research Center The University of Georgia AthensGA United States MGH & BWH Center for Clinical Data Science BostonMA United States

ISBN: (纸本)9783030597276

It has been of great interest in the neuroimaging community to model spatiotemporal brain function and disorders based on resting state functional magnetic resonance imaging (rfMRI). A variety of spatiotemporal methods have been proposed for rfMRI so far, including deep learning models such as convolution networks (CNN) and recurrent networks (RNN). However, the dominant models fail to capture the long-distance dependency (LDD) due to their sequential nature, which becomes critical at longer sequence lengths due to memory limit. Inspired by human brains extraordinary ability of long-term memory and attention, the attention mechanism is designed for machine translation to draw global dependencies and achieved state-of-the-art. In this paper, we propose a spatiotemporal attention autoencoder (STAAE) to discover global features that address LDDs in rfMRI. STAAE encodes the information throughout the rfMRI sequence and reveals resting state networks (RSNs) that characterize spatial and temporal properties of the data. Considering that the rfMRI is measured without external tasks, an unsupervised classification framework is developed based on the connectome generated with STAAE. This framework has been evaluated on 281 children with ADHD and 266 normal control children from 4 sites of ADHD200 datasets. The proposed STAAE reveals the global functional interaction in the brain and achieves a state-of-the-art classification accuracy from 59.5% to 77.2% on multiple sites. It is evident that the proposed attention-based model provides a novel approach towards better understanding of human brain. © 2020, Springer Nature Switzerland AG.

关键词： Brain

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