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XB-SIM*:A Simulation Framework for Modeling and Exploration of ReRAM-Based CNN Acceleration Design

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Tsinghua Science and Technology 2021年第3期26卷 322-334页

作者： Xiang Fei youhui zhang Weimin Zheng Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China Beijing National Research Center for Information Science and Technology Beijing 100084China

Resistive Random Access Memory(ReRAM)-based neural network accelerators have potential to surpass their digital counterparts in computational efficiency and ***,design of these accelerators faces a number of challenges including imperfections of the Re RAM device and a large amount of calculations required to accurately simulate the *** present XB-SIM,a simulation framework for Re RAM-crossbar-based Convolutional Neural Network(CNN)***-SIM can be flexibly configured to simulate the accelerator’s structure and clock-driven behaviors at the architecture *** framework also includes an Re RAM-aware Neural Network(NN)training algorithm and a CNN-oriented mapper to train an NN and map it onto the simulated design *** of the simulator has been verified by the corresponding circuit simulation of a real ***,a batch processing mode of the massive calculations that are required to mimic the behavior of Re RAM-crossbar circuits is proposed to fully apply the computational concurrency of the mapping *** CPU/GPGPU,this batch processing mode can improve the simulation speed by up to 5.02 or *** this framework,comprehensive architectural exploration and end-to-end evaluation have been achieved,which provide some insights for systemic optimization.

关键词： deep neural network Resistive Random Access Memory(Re RAM) simulation accelerator processing in memory

Towards“General Purpose”Brain-Inspired Computing System

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Tsinghua Science and Technology 2021年第5期26卷 664-673页

作者： youhui zhang Peng Qu Weimin Zheng the Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China

Brain-inspired computing refers to computational models,methods,and systems,that are mainly inspired by the processing mode or structure of brain.A recent study proposed the concept of"neuromorphic completeness"and the corresponding system hierarchy,which is helpful to determine the capability boundary of brain-inspired computing system and to judge whether hardware and software of brain-inspired computing are compatible with each *** a position paper,this article analyzes the existing brain-inspired chips design characteristics and the current so-called"general purpose"application development frameworks for brain-inspired computing,as well as introduces the background and the potential of this ***,some key features of this concept are presented through the comparison with the Turing completeness and approximate computation,and the analyses of the relationship with"general-purpose"brain-inspired computing systems(it means that computing systems can support all computable applications).In the end,a promising technical approach to realize such computing systems is introduced,as well as the on-going research and the work *** believe that this work is conducive to the design of extensible neuromorphic complete hardware-primitives and the corresponding *** this basis,it is expected to gradually realize"general purpose"brain-inspired computing system,in order to take into account the functionality completeness and application efficiency.

关键词： brain-inspired computing neuromorphic computing computational completeness hardware/software decoupling system hierarchy

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基于云计算的计算机网络安全研究

移动信息 2024年第4期46卷 167-169页

作者：卢毅张铕辉郑州城建职业学院郑州450000

云计算环境为数据共享提供了便利,但存在诸多计算机网络安全隐患。这些隐患主要涉及数据隐私泄露、多租户环境带来的安全挑战、虚拟化技术的安全漏洞、服务供应商的安全性、数据备份、灾难恢复等。针对这些安全隐患,需要采取一系列措施... 详细信息

云计算环境为数据共享提供了便利,但存在诸多计算机网络安全隐患。这些隐患主要涉及数据隐私泄露、多租户环境带来的安全挑战、虚拟化技术的安全漏洞、服务供应商的安全性、数据备份、灾难恢复等。针对这些安全隐患,需要采取一系列措施来加强网络安全,如加密数据,多层次安全防御,定期安全审计,加强虚拟化安全管理,建立灵活的灾备与恢复机制等。文中探讨了云计算环境下的网络安全隐患及解决方案,以提高云环境中的数据和信息的安全性、可靠性。

关键词：云计算环境计算机网络安全安全隐患

HW/SW Co-optimization for Stencil Computation：Beginning with a Customizable Core

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Tsinghua Science and Technology 2016年第5期21卷 570-580页

作者： Yanhua Li youhui zhang Weiming Zheng Department of Computer Science Tsinghua University Beijing 100084 China

Energy efficiency is one of the most important issues for High Performance Computing（HPC） *** HPC platform with some energy-efficient customizable cores（as application-specific accelerators）is believed as one of the promising solutions to meet ever-increasing computing needs and to overcome power density limitations. In this paper, we focus on using customizable processor cores to optimize the typical stencil computations—— the kernel of many high-performance applications. We develop a series of effective software/hardware co-optimization strategies to exploit the instruction-level and memory-computation parallelism,as well as to decrease the energy consumption. These optimizations include loop tiling, prefetching, cache customization, Single Instruction Multiple Data（SIMD）, and Direct Memory Access（DMA）, as well as necessary ISA extensions. Detailed tests of power-efficiency are given to evaluate the effect of all these optimizations comprehensively. The results are impressive： the combination of these optimizations has improved the application performance by 341% while the energy consumption has been decreased by 35%; a preliminary comparison with X86, GPU, and FPGA platforms also showed that the design could achieve an order of magnitude higher performance efficiency. We believe this work can help understand sources of inefficiency in general-purpose chips and can be used as a beginning to customize an energy efficient CMP for further improvement.

关键词： energy efficiency customizable processor stencil computation software and hardware co-optimization

Hardware Implementation of Spiking Neural Networks on FPGA

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Tsinghua Science and Technology 2020年第4期25卷 479-486页

作者： Jianhui Han Zhaolin Li Weimin Zheng youhui zhang the Institute of Microelectronics Tsinghua UniversityBeijing 100084China the Research Institute of Information Technology Tsinghua UniversityBeijing 100084China the Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China

Inspired by real biological neural models,Spiking Neural Networks(SNNs)process information with discrete spikes and show great potential for building low-power neural network *** paper proposes a hardware implementation of SNN based on Field-Programmable Gate Arrays(FPGA).It features a hybrid updating algorithm,which combines the advantages of existing algorithms to simplify hardware design and improve *** proposed design supports up to 16384 neurons and 16.8 million synapses but requires minimal hardware resources and archieves a very low power consumption of 0.477 W.A test platform is built based on the proposed design using a Xilinx FPGA evaluation board,upon which we deploy a classification task on the MNIST *** evaluation results show an accuracy of 97.06%and a frame rate of 161 frames per second.

关键词： Spiking Neural Network(SNN) Field-Programmable Gate Arrays(FPGA) digital circuit low-power MNIST

Employing intelligence in object-based storage devices to provide attribute-based file access

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Science China(Information Sciences) 2013年第3期56卷 169-178页

作者： zhang youhui QIAN ZiQiang ZHENG WeiMin Department of Computer Science and Technology Tsinghua University

Attribute-based file access has been regarded as a promising method to reach files based on their attributes rather than through the usual directory hierarchy,which is especially meaningful for locating the target among mass of ***,because the attribute-based file system introduces higher performance overhead and is usually incompatible with existing file access interfaces,till now there are few practical attribute-based file *** paper designs a file system with attribute-based access methods and a compatible access interface,using object-based storage devices(OSD).In detail,this design fully utilizes existing features of the OSD standard(T10):object attributes and access methods are mapped into file attributes and the corresponding access interfaces respectively;collection-objects are used for fast indexing of user ***,the attribute-based object organization can be achieved in OSD efficiently and the powerful processing capability of OSD is used to complete object-queries to speed up the ***,the way to export the new accessing methods through file system with a compatible interface is also *** a prototype has been *** show that it introduces much less overheads than some existing attribute-based file systems,and the query performance is higher than its counterpart based on the block-level device.

关键词： attribute-based file access object-based storage device intelligent storage

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陆地LNG工厂模块化建造涂装设计应用

涂料工业 2024年第8期54卷 65-69页

作者：刘存马永青韩明恩何宾张有慧海洋石油工程(青岛)有限公司山东青岛266520

液化天然气(LNG)作为绿色能源,其市场需求巨大。陆地LNG工厂建设具有大型化、规模化及集成化的特点,模块化建造能够达到缩短工期、降低成本、减少污染等要求。文中从防护涂层系统设计、涂装施工、涂层缺陷修补3个方面,对大型陆地LNG工... 详细信息

液化天然气(LNG)作为绿色能源,其市场需求巨大。陆地LNG工厂建设具有大型化、规模化及集成化的特点,模块化建造能够达到缩短工期、降低成本、减少污染等要求。文中从防护涂层系统设计、涂装施工、涂层缺陷修补3个方面,对大型陆地LNG工厂模块化建造过程中的涂层材料及涂层体系设计,有机涂层、金属涂层、防火涂料等设计标准及相关要求,以及涂装结构设计要求等进行针对性解读。结合实际涂装过程中施工人员认证考核、表面处理要求、涂装施工、热喷铝施工要求,及建造过程中涂层保护进行阐述;同时,对建造过程中有机涂层和金属涂层缺陷及修补方法等进行总结,提出了相应工程经验推荐做法,为从事相关工作人员提供参考。

关键词： LNG工厂模块化建造涂层设计涂装施工

A GSH-responsive PET-based fluorescent probe for cancer cells imaging

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Chinese Chemical Letters 2021年第4期32卷 1541-1544页

作者： Xue Li Huaying Wang youhui zhang Qianyong Cao Yong Chen Chemical College Nanchang UniversityNanchang 330031 China Jiangxi Key Laboratory for Microscale Interdisciplinary Study Institute for Advanced Study.Nanchang UniversityNanchang 330031China

An efficient PET-based probe,in which the ferrocene quencher and the naphthalimide fluorophore are linked by a disulfide bond,has been *** probe can be activated by GSH with fluorescence a turn-on response for blockin... 详细信息

关键词： GSH sensing Photoinduced electron transfer Fluorescent sensor Cancer cells imaging

AIPerf: Automated Machine Learning as an AI-HPC Benchmark

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Big Data Mining and Analytics 2021年第3期4卷 208-220页

作者： Zhixiang Ren Yongheng Liu Tianhui Shi Lei Xie Yue Zhou Jidong Zhai youhui zhang Yunquan zhang Wenguang Chen Peng Cheng National Laboratory Shenzhen 518000China Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China Institute of Computing Technology Chinese Academy of SciencesBeijing 100086China

The plethora of complex Artificial Intelligence(AI)algorithms and available High-Performance Computing(HPC)power stimulates the expeditious development of AI components with heterogeneous ***,the need for cross-stack performance benchmarking of AI-HPC systems has rapidly *** particular,the de facto HPC benchmark,LINPACK,cannot reflect the AI computing power and input/output performance without a representative *** popular AI benchmarks,such as MLPerf,have a fixed problem size and therefore limited *** address these issues,we propose an end-to-end benchmark suite utilizing automated machine learning,which not only represents real AI scenarios,but also is auto-adaptively scalable to various scales of *** implement the algorithms in a highly parallel and flexible way to ensure the efficiency and optimization potential on diverse systems with customizable *** utilize Operations Per Second(OPS),which is measured in an analytical and systematic approach,as a major metric to quantify the AI *** perform evaluations on various systems to ensure the benchmark’s stability and scalability,from 4 nodes with 32 NVIDIA Tesla T4(56.1 Tera-OPS measured)up to 512 nodes with 4096 Huawei Ascend 910(194.53 Peta-OPS measured),and the results show near-linear weak *** a flexible workload and single metric,AIPerf can easily scale on and rank AI-HPC,providing a powerful benchmark suite for the coming supercomputing era.

关键词： High-Performance Computing(HPC) Artificial Intelligence(AI) automated machine learning