CrashTest: A Fast High-Fidelity FPGA-Based Resiliency Analysis Framework

被引:38
|
作者
Pellegrini, Andrea [1 ]
Constantinides, Kypros [1 ]
Zhang, Dan [1 ]
Sudhakar, Shobana [1 ]
Bertacco, Valeria [1 ]
Austin, Todd [1 ]
机构
[1] Univ Michigan, Ann Arbor, MI 48109 USA
来源
2008 IEEE INTERNATIONAL CONFERENCE ON COMPUTER DESIGN | 2008年
关键词
D O I
10.1109/ICCD.2008.4751886
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Extreme scaling practices in silicon technology are quickly leading to integrated circuit components with limited reliability, where phenomena such as early-transistor failures, gate-oxide wearout, and transient faults are becoming increasingly common. In order to overcome these issues and develop robust design techniques for large-market silicon ICs, it is necessary to rely on accurate failure analysis frameworks which enable design houses to faithfully evaluate both the impact of a wide range of potential failures and the ability of candidate reliable mechanisms to overcome them. Unfortunately, while failure rates are already growing beyond economically viable limits, no fault analysis framework is yet available that is both accurate and can operate on a complex integrated system. To address this void, we present CrashTest, a fast, high-fidelity and flexible resiliency analysis system. Given a hardware description model of the design under analysis, CrashTest is capable of orchestrating and performing a comprehensive design resiliency analysis by examining how the design reacts to faults while running software applications. Upon completion, CrashTest provides a high-fidelity analysis report obtained by performing a fault injection campaign at the gate-level netlist of the design. The fault injection and analysis process is significantly accelerated by the use of an FPGA hardware emulation platform. We conducted experimental evaluations on a range of systems, including a complex LEON-based system-on-chip, and evaluated the impact of gate-level injected faults at the system level. We found that CrashTest is 16-90x faster than an equivalent software-based framework, when analyzing designs through direct primary I/Os. As shown by our LEON-based SoC experiments, CrashTest exhibits emulation speeds that are six orders of magnitude faster than simulation.
引用
收藏
页码:363 / 370
页数:8
相关论文
共 50 条
  • [41] A high-fidelity comprehensive framework for the additive manufacturing printability assessment
    Guo, Liping
    Liu, Hanjie
    Wang, Hongze
    Wei, Qianglong
    Zhang, Jiahui
    Chen, Yingyan
    Leung, Chu Lun Alex
    Lian, Qing
    Wu, Yi
    Zou, Yu
    Wang, Haowei
    JOURNAL OF MANUFACTURING PROCESSES, 2023, 105 : 219 - 231
  • [42] An FPGA-based hardware emulator for fast fault emulation
    Hong, JH
    Hwang, SA
    Wu, CW
    PROCEEDINGS OF THE 39TH MIDWEST SYMPOSIUM ON CIRCUITS AND SYSTEMS, VOLS I-III, 1996, : 345 - 348
  • [43] A Framework for High-Fidelity and Efficient Collapse Fragility Estimation of Structures Using Cluster Analysis
    Liu, Bali
    Hu, Jinjun
    Liu, Yiheng
    JOURNAL OF EARTHQUAKE ENGINEERING, 2024, 28 (16) : 4749 - 4766
  • [44] A framework for high-fidelity particle tracking on massively parallel systems
    Kopper, Patrick
    Schwarz, Anna
    Copplestone, Stephen M.
    Ortwein, Philip
    Staudacher, Stephan
    Beck, Andrea
    COMPUTER PHYSICS COMMUNICATIONS, 2023, 289
  • [45] An Adaptive High-Fidelity Image Compression Framework for Internet of Vehicles
    Gad, Ahmed
    Nayak, Amiya
    IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC 2022), 2022, : 401 - 406
  • [46] An FPGA-based eigenfilter using fast Hebbian learning
    Lam, KP
    Mak, ST
    2003 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOL II, PROCEEDINGS: SPEECH II; INDUSTRY TECHNOLOGY TRACKS; DESIGN & IMPLEMENTATION OF SIGNAL PROCESSING SYSTEMS; NEURAL NETWORKS FOR SIGNAL PROCESSING, 2003, : 765 - 768
  • [47] An Active Learning Framework for Constructing High-Fidelity Mobility Maps
    Marple, Gary R.
    Gorsich, David
    Jayakumar, Paramsothy
    Veerapaneni, Shravan
    IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2021, 70 (10) : 9803 - 9813
  • [48] A Fast and Accurate FPGA-Based Fault Injection System
    Schweizer, Thomas
    Peterson, Dustin
    Kuehn, Johannes M.
    Kuhn, Tommy
    Rosenstiel, Wolfgang
    2013 IEEE 21ST ANNUAL INTERNATIONAL SYMPOSIUM ON FIELD-PROGRAMMABLE CUSTOM COMPUTING MACHINES (FCCM), 2013, : 236 - 236
  • [49] FAST: FPGA-based Subgraph Matching on Massive Graphs
    Jin, Xin
    Yang, Zhengyi
    Lin, Xuemin
    Yang, Shiyu
    Qin, Lu
    Peng, You
    2021 IEEE 37TH INTERNATIONAL CONFERENCE ON DATA ENGINEERING (ICDE 2021), 2021, : 1452 - 1463
  • [50] AdapNoC: A Fast and Flexible FPGA-based NoC Simulator
    Kamali, Hadi Mardani
    Hessabi, Shahin
    2016 26TH INTERNATIONAL CONFERENCE ON FIELD PROGRAMMABLE LOGIC AND APPLICATIONS (FPL), 2016,