Instruction-Level NBTI Stress Estimation and Its Application in Runtime Aging Prediction for Embedded Processors

Lifetime reliability management of miniaturized CMOS devices continuously gets more importance with the shrinking of technology size. Neither of existing design-time solutions (like guard-banding) and runtime methods (like reactive monitoring) does efficiently address this issue; rather, proactive a...

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Bibliographic Details
Published in:IEEE transactions on computer-aided design of integrated circuits and systems 2019-08, Vol.38 (8), p.1427-1437
Main Authors: Moghaddasi, Iraj, Fouman, Arash, Salehi, Mostafa E., Kargahi, Mehdi
Format: Article
Language:English
Subjects:
Aging
CMOS
Computer simulation
Degradation
Electric potential
Embedded systems
Fine-grained stress estimation
Model accuracy
Negative bias temperature instability
negative bias temperature instability (NBTI)
Processors
Program processors
regression analysis
Reliability
Runtime
runtime aging prediction
Stability
Stress
Thermal variables control
voltage and temperature variation
workload monitoring
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Summary:Lifetime reliability management of miniaturized CMOS devices continuously gets more importance with the shrinking of technology size. Neither of existing design-time solutions (like guard-banding) and runtime methods (like reactive monitoring) does efficiently address this issue; rather, proactive approaches, which use runtime aging prediction, are getting more promising to provide resiliency. Among various reliability threatening mechanisms in recent technologies, negative bias temperature instability is the dominant factor; it depends on multiple time-varying operational parameters, including temperature, supply voltage, and stress. This paper proposes an efficient instruction-level stress estimation model; accordingly, it introduces a runtime aging prediction approach for embedded processors, taking simultaneous impacts of the temperature, supply voltage, and stress variations. We propose instruction degradation factor and architecture degradation factor metrics, respectively, for fine-grained stress estimation and recurring runtime aging prediction. We also provide a simulation environment for model validation. Simulation results of several benchmarks show that the proposed stress estimation model has an accuracy of about 92%, indicating that the method is accurate enough, yet simple for runtime usage.
ISSN:0278-0070
1937-4151
DOI:10.1109/TCAD.2018.2846629