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IEEE International Superconductive Electronics Conference (ISEC)

作者： Pei-Shi Yu Guang-Ming Tang Xiao-Chun Ye Dong-Rui Fan Zhi-Min Zhang Ning-Hui Sun University of Chinese Academy of Sciences Beijing China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China

An 8-bit bit-slice TEA-cryptographic accelerator for 64-bit RSFQ secure coprocessors is proposed. The accelerator is based on Tiny Encryption Algorithm (TEA) and mainly consists of bit-slice adders and bit-slice shifters. Synchronous concurrent-flow clocking is used to design a fully pipelined RSFQ logic design. For verifying the algorithm and the logic design, the RSFQ logic circuits of the proposed accelerator have been simulated with a target operating frequency of 50 GHz. It consists of 21 stages. The throughput is 7.672 × 10 7 64-bit TEA encryptions per second.

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Utilizing the instability in weakly supervised object detection

arXiv

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arXiv 2019年

作者： Gao, Yan Liu, Boxiao Guo, Nan Ye, Xiaochun Wan, Fang You, Haihang Fan, Dongrui State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

Weakly supervised object detection (WSOD) focuses on training object detector with only image-level annotations, and is challenging due to the gap between the supervision and the objective. Most of existing approaches model WSOD as a multiple instance learning (MIL) problem. However, we observe that the result of MIL based detector is unstable, i.e., the most confident bounding boxes change significantly when using different initializations. We quantitatively demonstrate the instability by introducing a metric to measure it, and empirically analyze the reason of instability. Although the instability seems harmful for detection task, we argue that it can be utilized to improve the performance by fusing the results of differently initialized detectors. To implement this idea, we propose an end-to-end framework with multiple detection branches, and introduce a simple fusion strategy. We further propose an orthogonal initialization method to increase the difference between detection branches. By utilizing the instability, we achieve 52.6% and 48.0% mAP on the challenging PASCAL VOC 2007 and 2012 datasets, which are both the new state-of-the-arts. Copyright © 2019, The Authors. All rights reserved.

关键词： Stability

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When Deep Learning Meets the Edge: Auto-Masking Deep Neural Networks for Efficient Machine Learning on Edge Devices

When Deep Learning Meets the Edge: Auto-Masking Deep Neural ...

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IEEE International Conference on computer Design: VLSI in computers and Processors, (ICCD)

作者： Ning Lin Hang Lu Xing Hu Jingliang Gao Mingzhe Zhang Xiaowei Li State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Department of Electrical and Computer Engineering University of California Santa Barbara

Deep neural network (DNN) has demonstrated promising performance in various machine learning tasks. Due to the privacy issue and the unpredictable transmission latency, inferring DNN models directly on edge devices trends the development of intelligent systems, like self-driving cars, smart Internet-of-Things (IoTs) and autonomous robotics. The on-device DNN model is obtained by expensive training via vast volumes of high-quality training data in the cloud datacenter, and then deployed into these devices, expecting it to work effectively at the edge. However, edge device always deals with low-quality images caused by compression or environmental noise pollutions. The well-trained model, though could work perfectly on the cloud, cannot adapt to these edge-specific conditions without remarkable accuracy drop. In this paper, we propose an automated strategy, called "AutoMask", to embrace effective machine learning and accelerate DNN inference on edge devices. AutoMask comprises end-to-end trainable software strategies and cost-effective hardware accelerator architecture to improve the adaptability of the device without compromising the constrained computation and storage resources. Extensive experiments, over ImageNet dataset and various state-of-the-art DNNs, show that AutoMask achieves significant inference acceleration and storage reduction while maintains comparable accuracy level on embedded Xilinx Z7020 FPGA, as well as NVIDIA Jetson TX2.

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Register-based implementation of the sparse general matrix-matrix multiplication on GPUs

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ACM SIGPLAN Notices 2018年第1期53卷 407-408页

作者： Liu, Junhong He, Xin Liu, Weifeng Tan, Guangming State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China Department of Computer Science Norwegian University of Science and Technology Norway

General sparse matrix-matrix multiplication (SpGEMM) is an essential building block in a number of applications. In our work, we fully utilize GPU registers and shared memory to implement an efficient and load balance... 详细信息

ISBN: (纸本)9781450349116

关键词： Matrix algebra

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Influence of high-energy local orbitals and electron-phonon interactions on the band gaps and optical absorption spectra of hexagonal boron nitride

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Physical Review B 2020年第4期102卷 045117-045117页

作者： Tong Shen Xiao-Wei Zhang Honghui Shang Min-Ye Zhang Xinqiang Wang En-Ge Wang Hong Jiang Xin-Zheng Li State Key Laboratory for Artificial Microstructure and Mesoscopic Physics Frontier Science Center for Nano-optoelectronics and School of Physics Peking University Beijing 100871 China International Center for Quantum Materials and School of Physics Peking University Beijing 100871 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing 100871 China Beijing National Laboratory for Molecular Sciences College of Chemistry and Molecular Engineering Peking University Beijing 100871 China Collaborative Innovation Center of Quantum Matter Peking University Beijing 100871 China Ceramic Division Songshan Lake Lab Institute of Physics Chinese Academy of Sciences Guangdong 523808 China School of Physics Liaoning University Shenyang 110036 China

We report ab initio band diagram and optical absorption spectra of hexagonal boron nitride (h-BN), focusing on unravelling how the completeness of the basis set for GW calculations and electron-phonon interactions (EPIs) impact on them. The completeness of the basis set, an issue which was seldom discussed in previous optical spectra calculations of h-BN, is found crucial in providing converged quasiparticle band gaps. In the comparison among three different codes, we demonstrate that by including high-energy local orbitals in the all-electron linearized augmented plane waves based GW calculations, the quasiparticle direct and fundamental indirect band gaps are widened by ∼0.2 eV, giving values of 6.81 eV and 6.25 eV, respectively at the GW0 level. EPIs, on the other hand, reduce them to 6.62 eV and 6.03 eV respectively at 0 K, and 6.60 eV and 5.98 eV respectively at 300 K. With clamped crystal structure, the first peak of the absorption spectrum is at 6.07 eV, originating from the direct exciton contributed by electron transitions around K in the Brillouin zone. After including the EPIs-renormalized quasiparticles in the Bethe-Salpeter equation, the exciton-phonon coupling shifts the first peak to 5.83 eV at 300 K, lower than the experimental value of ∼6.00 eV. This accuracy is acceptable to an ab initio description of excited states with no fitting parameter.

关键词： Bethe-Salpeter equation Electron-phonon coupling Excitons GW method

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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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Efficient parallel linear scaling method to get the response density matrix in all-electron real-space density-functional perturbation theory

arXiv

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arXiv 2020年

作者： Shang, Honghui Liang, Wanzhen Zhang, Yunquan Yang, Jinlong State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China State Key Laboratory of Physical Chemistry of Solid Surfaces Collaborative Innovation Center of Chemistry for Energy Materials Fujian Provincial KeyLaboratory of Theoretical and Computational Chemistry Department of Chemistry College of Chemistry and Chemical Engineering Xiamen University XiamenFujian361005 China Hefei National Laboratory for Physical Sciences at Microscale Department of Chemical Physics Synergetic Innovation Center of Quantum Information and Quantum Physics University of Science and Technology of China Hefei Anhui230026 China

The real-space density-functional perturbation theory (DFPT) for the computations of the response properties with respect to the atomic displacement and homogeneous electric field perturbation has been recently developed and implemented into the all-electron, numeric atom-centered orbitals electronic structure package FHI-aims. It is found that the bottleneck for large scale applications is the computation of the response density matrix, which scales as O(N3). Here for the response properties with respect to the homogeneous electric field, we present an efficient parallel linear scaling algorithm for the response density matrix calculation. Our scheme is based on the second-order trace-correcting purification and the parallel sparse matrix-matrix multiplication algorithms. The new scheme reduces the formal scaling from O(N3) to O(N), and shows good parallel scalability over tens of thousands of cores. As demonstrated by extensive validation, we achieve a rapid computation of accurate polarizabilities using DFPT. Finally, the computational efficiency of this scheme has been illustrated by making the scaling tests and scalability tests on massively parallel computer systems. Copyright © 2020, The Authors. All rights reserved.

关键词： Matrix algebra

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AIBench: An Agile Domain-specific Benchmarking Methodology and an AI Benchmark Suite

arXiv

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arXiv 2020年

作者： Gao, Wanling Tang, Fei Zhan, Jianfeng Lan, Chuanxin Luo, Chunjie Wang, Lei Dai, Jiahui Cao, Zheng Xiong, Xiongwang Jiang, Zihan Hao, Tianshu Fan, Fanda Wen, Xu Zhang, Fan Huang, Yunyou Chen, Jianan Du, Mengjia Ren, Rui Zheng, Chen Zheng, Daoyi Tang, Haoning Zhan, Kunlin Wang, Biao Kong, Defei Yu, Minghe Tan, Chongkang Li, Huan Tian, Xinhui Li, Yatao Lu, Gang Shao, Junchao Wang, Zhenyu Wang, Xiaoyu Ye, Hainan State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences BenchCouncil [International Open Benchmarking Council Beijing Academy of Frontier Sciences and Technology University of Chinese Academy of Sciences Alibaba Baidu Tencent *** NetEase ByteDance Zhihu Lenovo Paypal Moqi Microsoft Research Asia Huawei *** CloudTa Intellifusion

Domain-specific software and hardware co-design is encouraging as it is much easier to achieve efficiency for fewer tasks. Agile domain-specific benchmarking speeds up the process as it provides not only relevant design inputs but also relevant metrics, and tools. Unfortunately, modern workloads like Big data, AI, and Internet services dwarf the traditional one in terms of code size, deployment scale, and execution path, and hence raise serious benchmarking challenges. This paper proposes an agile domain-specific benchmarking methodology. Together with seventeen industry partners, we identify ten important end-to-end application scenarios, among which sixteen representative AI tasks are distilled as the AI component benchmarks. We propose the permutations of essential AI and non-AI component benchmarks as end-to-end benchmarks. An end-to-end benchmark is a distillation of the essential attributes of an industry-scale application. We design and implement a highly extensible, configurable, and flexible benchmark framework, on the basis of which, we propose the guideline for building end-to-end benchmarks, and present the first end-to-end Internet service AI benchmark. The preliminary evaluation shows the value of our benchmark suite-AIBench against MLPerf and TailBench for hardware and software designers, micro-architectural researchers, and code developers. The specifications, source code, testbed, and results are publicly available from the web site http: //***/AIBench/***. Copyright © 2020, The Authors. All rights reserved.

关键词： Benchmarking

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Efficiently Inferring Top-k Elephant Flows based on Discrete Tensor Completion

Efficiently Inferring Top-k Elephant Flows based on Discrete...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Kun Xie Jiazheng Tian Xin Wang Gaogang Xie Jigang Wen Dafang Zhang College of Computer Science and Electronics Engineering Hunan University Changsha China Department of Electrical and Computer Engineering State University of New York at Stony Brook USA State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences

Finding top- k elephant flows is a critical task in network measurement, with applications such as congestion control, anomaly detection, and traffic engineering. Traditional top- k flow detection problem focuses on using a small amount of memory to measure the total number of packets or bytes of each flow. Instead, we study a challenging problem of inferring the top- k elephant flows in a practical system with incomplete measurement data as a result of sub-sampling for scalability or data missing. The recent study shows it is promising to more accurately interpolate the missing data with a 3-D tensor compared to that based on a 2-D matrix. Taking full advantage of the multilinear structures, we apply tensor completion to first recover the missing data and then find the top- k elephant flows. To reduce the computational overhead, we propose a novel discrete tensor completion model which uses binary codes to represent the factor matrices. Based on the model, we further propose three novel techniques to speed up the whole top- k flow inference process: a discrete optimization algorithm to train the binary factor matrices, bit operations to facilitate quick missing data inference, and simplifying the finding of top- k elephant flows with binary code partition. In our discrete tensor completion model, only one bit is needed to represent the entry in the factor matrices instead of a real value (32 bits) needed in traditional tensor completion model, thus the storage cost is reduced significantly. Extensive experiments using two real traces demonstrate that compared with the state of art tensor completion algorithms, our discrete tensor completion algorithm can achieve similar data inference accuracy using significantly smaller time and storage space.

关键词： Inference algorithms Binary codes Data models Partitioning algorithms Indexes Computational modeling

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Online Internet Anomaly Detection With High Accuracy: A Fast Tensor Factorization Solution

Online Internet Anomaly Detection With High Accuracy: A Fast...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Xiaocan Li Kun Xie Xin Wang Gaogang Xie Jigang Wen Guangxing Zhang Zheng Qin Department of Electrical and Computer Engineering State University of New York at Stony Brook USA College of Computer Science and Electronics Engineering Hunan University Changsha China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences

Traffic anomaly detection is critical for advanced Internet management. Existing detection algorithms usually work off-line and cannot timely detect anomalies. They also suffer from high cost for storage and computation. Although online and accurate traffic anomaly detection is very important, it very difficult to achieve. We propose to utilize tensor model to well exploit the multi-dimensional information hidden in the traffic data for more accurate online Internet anomaly detection. We decouple the tensor recovery problem to iteratively solve two sub problems, a tensor factorization sub-problem and an anomaly detection sub-problem. To reduce the high cost for computation and storage involved in tensor factorization, we propose two lightweight techniques to effectively derive factor matrices of tensor in the current window and iteration, taking advantage of tensor decomposition results of the previous window and iteration. We have done extensive experiments using two real traffic traces to compare with three tensor based algorithms and three matrix based algorithms. The experiment results demonstrate that our online anomaly detection algorithm can achieve the same anomaly detection accuracy as that of the best offline tensor based algorithm, but at 6100 times faster speed and with very low storage cost.

关键词： Anomaly detection Monitoring Matrix decomposition Microsoft Windows Principal component analysis Data models

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