NDLI: A confidence-driven model for error-resilient computing

Content Provider	IEEE Xplore Digital Library
Author	Chia-Hsiang Chen Yejoong Kim Zhengya Zhang Blaauw, D. Sylvester, D. Naeimi, H. Sandhu, S.
Copyright Year	2011
Description	Author affiliation: Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI (Chia-Hsiang Chen; Yejoong Kim; Zhengya Zhang; Blaauw, D.; Sylvester, D.) \|\| Intel Corporation, Santa Clara, CA (Naeimi, H.; Sandhu, S.)
Abstract	We propose an adaptive reliability enhancement structure for deeply-scaled CMOS and future devices that exhibit nondeterministic behavior. This structure forms the basis of a confidence-driven computing model that can be implemented in either a rollback recovery or an iterative dual modular redundancy method incorporating synchronous handshake schemes. The performance and cost of the computing model are estimated using a 45 nm CMOS technology and the functionality is verified by FPGA-based emulation. The confidence-driven computing model is demonstrated using a 16-bit, 12-stage CORDIC processor operating under random, transient errors. The confidence-driven computing model adapts to the fluctuating error rates at the device substrate level to guarantee the reliability of computation at the system level. This computing model costs 4.2 times smaller area and 2.7 times less energy overhead than triple modular redundancy to guarantee a system-level mean time to failure of two years.
Starting Page	1
Ending Page	6
File Size	170852
Page Count	6
File Format	PDF
ISBN	9781612842080
ISSN	15301591
e-ISBN	9783981080186
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-03-14
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	European Design Automation Association (EDAA)
Subject Keyword	Error analysis Redundancy Synchronization Delay Throughput Computational modeling dual modular redundancy reliability transient error confidence estimator rollback recovery
Content Type	Text
Resource Type	Article

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