Stochastic thermal simulation considering spatial correlated within-die process variations

Huang, Pei-Yu; Wu, Jianhua; Lee, Yu-Min

doi:10.1109/aspdac.2009.4796437

Cited by 4 publications

(17 citation statements)

References 13 publications

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“…The description given above is delivered from any explicit formula of any particular process parameter. In contrast, the solutions from the literature related to Probability density Gaussian(0, 1) Beta (8, 8, −4, 4) Uncertain parameter Figure 5.3: Beta distribution fitted to the standard Gaussian distribution process variation are typically based on ad hoc expressions and should be individually tailored by the designer to each new parameter; see, for instance, [7,44,51]. The proposed framework provides great flexibility in this regard.…”

Section: Surrogate Constructionmentioning

confidence: 76%

“…First, certain procedures cannot be undertaken without knowledge of time-dependent variations; one example of this is reliability optimization concerned with thermal-cycling fatigue, which is discussed in Section 3.6. Second, the static steady-state assumption that is considered, for instance, in [51,58,59,69] can rarely be justified, since power is not invariant in reality.…”

Section: Previous Workmentioning

confidence: 99%

“…Therefore, such variations are often conveniently modeled using Gaussian random variables [6,11,44,51,58,59,69,99,103]. Due to the underlying physics and for demonstration purposes, we take a step further and embed into the model the fact that the effective channel length-occupying the space between the drain and source of a transistor-cannot be arbitrarily large or take negative values, which Gaussian distributions allow it to do.…”

Section: Problem Formulationmentioning

confidence: 99%

“…To summarize, the prior techniques for probabilistic power and temperature analysis are restricted in use due to one or several of the following traits: (a) based on mc simulations (potentially slow) [11], (b) limited to power analysis [6,7,11,44,99,117], (c) ignoring the power-temperature interplay [6,7,44,51,99,117], (d) limited to the static steady-state temperature [51,58,59,69], (e) exclusive focus on the maximum temperature [59], and (f ) a priori chosen distributions of power and temperature [6,58,103]. The designer's toolbox does not yet include tools for transient analysis and dynamic steady-state analysis under process variation, which are of great importance for certain applications.…”

Section: Analysis and Design Under Process Uncertaintymentioning

confidence: 99%

“…Temperature-related issues originating from process variation are also considered in [58] where a statistical model of the static steadystate temperature is derived based on the linearity of Gaussian distributions and time-invariant systems. A stochastic temperature simulator targeted at the static steady state is developed in [51] using the polynomial chaos (pc) decomposition and the continuous Karhunen-Loève (kl) decomposition; see Appendix A.4 and Appendix A.7. A stochastic collocation [68,120] approach to static steady-state analysis is presented in [69], which relies on the kl decomposition and on Newton polynomials for interpolation.…”

Section: Previous Workmentioning

confidence: 99%

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System-Level Analysis and Design under Uncertainty

Ukhov¹

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One major problem for the designer of electronic systems is the presence of uncertainty, which is due to phenomena such as process and workload variation. Very often, uncertainty is inherent and inevitable. If ignored, it can lead to degradation of the quality of service in the best case and to severe faults or burnt silicon in the worst case. Thus, it is crucial to analyze uncertainty and to mitigate its damaging consequences by designing electronic systems in such a way that uncertainty is effectively and efficiently taken into account.We begin by considering techniques for deterministic system-level analysis and design of certain aspects of electronic systems. These techniques do not take uncertainty into account, but they serve as a solid foundation for those that do. Our attention revolves primarily around power and temperature, as they are of central importance for attaining robustness and energy efficiency. We develop a novel approach to dynamic steady-state temperature analysis of electronic systems and apply it in the context of reliability optimization.We then proceed to develop techniques that address uncertainty. The first technique is designed to quantify the variability in process parameters, which is induced by process variation, across silicon wafers based on indirect and potentially incomplete and noisy measurements. The second technique is designed to study diverse system-level characteristics with respect to the variability originating from process variation. In particular, it allows for analyzing transient temperature profiles as well as dynamic steady-state temperature profiles of electronic systems. This is illustrated by considering a problem of design-space exploration with probabilistic constraints related to reliability. The third technique that we develop is designed to efficiently tackle the case of sources of uncertainty that are less regular than process variation, such as workload variation. This technique is exemplified by analyzing the effect that workload units with uncertain processing times have on the timing-, power-, and temperature-related characteristics of the system under consideration.We also address the issue of runtime management of electronic systems that are subject to uncertainty. In this context, we perform an early investigation into the utility of advanced prediction techniques for the purpose of finegrained long-range forecasting of resource usage in large computer systems.All the proposed techniques are assessed by extensive experimental evaluations, which demonstrate the superior performance of our approaches to analysis and design of electronic systems compared to existing techniques. The research presented in this thesis has been partially funded by the National Computer Science Graduate School (cugs) in Sweden.v Sammanfattning Ett stort problem för designern inom elektroniska system är förekomsten av osäkerhet, som beror på sådana fenomen som variationer relaterade till tillverkning och arbetsbelastning. Osäkerhet är i många fall naturlig och oundv...

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Section: Surrogate Constructionmentioning

confidence: 76%

Section: Previous Workmentioning

confidence: 99%