Toward Physically-Adaptive Computing

Zick, Klaus; Hayes, John P.

doi:10.1109/saso.2010.13

Cited by 2 publications

(3 citation statements)

References 26 publications

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“…This study suggests future research directions involving upsetability in nanoscale systems, methods of selfcharacterization and optimization, and frameworks for physical adaptation [4]. As the amount of variation and uncertainty continues to grow, methods of physical selfadaptation will be increasingly important for achieving efficient and reliable field programmable systems.…”

Section: Discussionmentioning

confidence: 93%

“…All of these limitations point to the need for handling variations in-system via methods such as periodic self-test and self-optimization. We refer to such a paradigm as physically-adaptive computing [3] [4]. An ability to physically adapt will be increasingly valuable, particularly for applications constrained by power, energy or reliability; such applications are prevalent in the highperformance, consumer, and embedded domains.…”

Section: Introductionmentioning

confidence: 99%

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Self-Test and Adaptation for Random Variations in Reliability

Zick

Hayes

2010

2010 International Conference on Field Programmable Logic and Applications

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Random physical variations and noise are growing challenges for advanced electronic systems. Field programmable systems can, in principle, adapt to these phenomena, but two main problems must be addressed: how to efficiently characterize random variations and how to perform subsequent optimization. This paper addresses both of these questions. First, an approach to self-test is presented that uses on-chip noise emulation to quickly characterize some of the hidden variations in latches. Our noise-injection experiments demonstrate that there can be significant spreads in latch reliability even with current 65nm field-programmable gate arrays (FPGAs). We detected coefficients of variation as high as 77%. Second, we propose an approach to self-optimization using local resource swapping. Experiments on two FPGAs show improvements in mean-time-between-failures (MTBF) of up to 60%.

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Section: Discussionmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Self-Test and Adaptation for Random Variations in Reliability

Zick

Hayes

2010

2010 International Conference on Field Programmable Logic and Applications

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“…While electronic systems must typically compute precise results, reconfigurable systems can in principle change how they compute. We refer to such a paradigm as physically adaptive computing [Zick and Hayes 2010c].…”

Section: Introductionmentioning

confidence: 99%

Low-cost sensing with ring oscillator arrays for healthier reconfigurable systems

Zick

Hayes

2012

ACM Trans. Reconfigurable Technol. Syst.

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Electronic systems on a chip increasingly suffer from component variation, voltage noise, thermal hotspots, and other subtle physical phenomena. Systems with reconfigurability have unique opportunities for adapting to such effects. Required, however, are low-cost, fine-grained methods for sensing physical parameters. This article presents powerful, novel approaches to online sensing, including methods for designing compact reconfigurable sensors, low-cost threshold detection, and several enhanced measurement procedures. Together, the approaches help enable systems to autonomously uncover a wealth of physical information. A highly efficient counter and improved ring oscillator are introduced, enabling an entire sensor node in just 8 Virtex-5 LUTs. We describe how variations can be measured in delay, temperature, switching-induced IR drop, and leakage-induced IR drop. We demonstrate the proposed approach with an experimental system based on a Virtex-5, instrumented with over 100 sensors at an overhead of only 1.3%. Results from thermally controlled experiments provide some surprising insights and illustrate the utility of the approach. Online sensing can help open the door to physically adaptive computing, including fine-grained power, reliability, and health management schemes for systems on a chip. ACM Reference Format:Zick, K. M. and Hayes, J. P. 2012. Low-cost sensing with ring oscillator arrays for healthier reconfigurable systems.

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Toward Physically-Adaptive Computing

Cited by 2 publications

References 26 publications

Self-Test and Adaptation for Random Variations in Reliability

Self-Test and Adaptation for Random Variations in Reliability

Low-cost sensing with ring oscillator arrays for healthier reconfigurable systems

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