The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
A Self-Tuning DVS Processor Using Delay-Error Detection and Correction
Abstract
In this paper, we present a dynamic voltage scaling (DVS) technique called Razor which incorporates an in situ error detection and correction mechanism to recover from timing errors. We also present the implementation details and silicon measurements results of a 64-bit processor fabricated in 0.18-/spl mu/m technology that uses Razor for supply voltage control. Traditional DVS techniques require significant voltage safety margins to guarantee computational correctness at the worst case combination of process, voltage and temperature conditions, leading to a loss in energy efficiency. In Razor-based DVS, however, the supply voltage is automatically reduced to the point of first failure using the error detection and correction mechanism, thereby eliminating safety margins while still ensuring correct operation. In addition, the supply voltage can be intentionally scaled below the point of first failure of the processor to achieve an optimal tradeoff between energy savings from further voltage reduction and energy overhead from increased error detection and correction activity. We tested and measured savings due to Razor DVS for 33 different dies and obtained an average energy savings of 50% over worst case operating conditions by scaling supply voltage to achieve a 0.1% targeted error rate, at a fixed frequency of 120 MHz.
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Chandrakasan, A. P., Sheng, S., & Brodersen, R. W. (2015) · Cited by 2,232
Chandrakasan, A. P., Sheng, S., & Brodersen, R. W. (1992) · IEEE Journal of Solid-State Circuits · doi:10.1109/4.126534 · Cited by 2,156
Razor: a low-power pipeline based on circuit-level timing speculation
Ernst, D., Kim, N. S., Das, S., Pant, S., Rao, R. R., Pham, T. T., Ziesler, C. H., Blaauw, D. T., Austin, T. M., Flautner, K., et al. (2003) · doi:10.5555/956417.956571 · Cited by 1,072
Self-citationA dynamic voltage scaled microprocessor system
Burd, T. D., Pering, T. A., Stratakos, A. J., & Brodersen, R. W. (2000) · IEEE Journal of Solid-State Circuits · doi:10.1109/4.881202 · Cited by 916
Razor: a low-power pipeline based on circuit-level timing speculation
Ernst, D., Kim, N. S., Das, S., Pant, S., Rao, R., Pham, T., Ziesler, C., Blaauw, D., Austin, T., Flautner, K., et al. (2004) · 22nd Digital Avionics Systems Conference. Proceedings (Cat. No.03CH37449) · doi:10.1109/micro.2003.1253179 · Cited by 858
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Cited by
Minerva: enabling low-power, highly-accurate deep neural network accelerators
Reagen, B., Whatmough, P., Adolf, R., Rama, S., Lee, H., Lee, S. K., Hernández-Lobato, J. M., Wei, G.-Y., & Brooks, D. (2016) · ACM SIGARCH Computer Architecture News · doi:10.1145/3007787.3001165 · Cited by 446
Self-citationA survey on modeling and improving reliability of DNN algorithms and accelerators
Mittal, S. (2020) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2019.101689 · Cited by 175
Metadata
- Authors
- Das, S., Roberts, D., Lee, S., Pant, S., Blaauw, D., Austin, T., Flautner, K., & Mudge, T.
- Venue
- IEEE Journal of Solid-State Circuits
- Published
- 2006-04
- Volume
- 41
- Issue
- 4
- Pages
- 792-804
- ISSN
- 0018-9200 (ISSN-L)
- OpenAlex
- W2102587899
- Cited by
- 396
- References
- 5
- FWCI
- 25.64
- Type
- journal article
- Language
- en
- Open access
- closed
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