David I. August
Professor in the Department of Computer Science, Princeton University
Affiliated with the Department of Electrical Engineering, Princeton University
Ph.D. May 2000, Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign

Office: Computer Science Building Room 221
Email: august@princeton.edu
PGP: Public Key
PGP Fingerprint: DD96 3B12 7DA1 EF4E 46EE A23A D2AB 4FCE B365 2C9A
Fax: (609) 964-1699
Administrative Assistant: Pamela DelOrefice, (609) 258-5551

Front Page Publication List (with stats) Curriculum Vitae (PDF) The Liberty Research Group


Design and Evaluation of Hybrid Fault-Detection Systems [abstract] (IEEE Xplore, PDF)
George A. Reis, Jonathan Chang, Neil Vachharajani, Ram Rangan, David I. August, and Shubhendu S. Mukherjee
Proceedings of the 32nd International Symposium on Computer Architecture (ISCA), June 2005.
Accept Rate: 23% (45/194).

To improve performance and reduce power consumption, processor designers employ advances that shrink feature sizes, lower voltage levels, reduce noise margins, and increase clock rates. However, these advances also make processors more susceptible to transient faults that can affect program correctness. Up to now, system designers have primarily considered hardware-only and software-only fault-detection mechanisms to identify and mitigate the deleterious effects of transient faults. These two fault-detection systems, however, are extremes in the design space, representing sharp trade-offs between hardware cost, reliability, and performance.

In this paper, we identify hybrid hardware/software fault-detection mechanisms as promising alternatives to hardware- only and software-only systems. These hybrid systems offer designers more options to fit their reliability needs within their hardware and performance budgets. We propose CRAFT, a suite of three such hybrid techniques, to illustrate the potential of the hybrid approach. We evaluate CRAFT in relation to existing hardware and software reliability techniques. For fair, quantitative comparisons among hardware, software, and hybrid systems, we introduce a new metric, mean work to failure, which is able to compare systems for which machine instructions do not represent a constant unit of work. Additionally, we present a new simulation framework which rapidly assesses reliability and does not depend on manual identification of failure modes. Our evaluation illustrates that CRAFT, and hybrid techniques in general, offer attractive options in the fault-detection design space.