Neural network based on-chip thermal simulator

Kumar, Pratyush; Atienza, David

doi:10.1109/iscas.2010.5537439

Cited by 13 publications

(26 citation statements)

References 16 publications

(10 reference statements)

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“…Our NN model is a direct-method based simulator and hence, contrary to iterative techniques, the results are immediate. The authors in [17] already used a very different NN-based method for thermal modeling of a planar chip. However, their model is not applicable to 3-D ICs and has been designed for run-time thermal management.…”

Section: Related Workmentioning

confidence: 99%

“…NN is an information processing method that was inspired by the way biological nervous systems, such as the brain, function to process information [16]. It is composed of a large number of highly interconnected processing elements (neurons) working in unison, and can be trained to solve complex problems, such as thermal modeling of specific IC layouts [17]. On one hand, NNs are very flexible and can be trained to mimic the behavior of any physical system with relative ease of implementation.…”

Section: Introductionmentioning

confidence: 99%

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Neural Network-Based Thermal Simulation of Integrated Circuits on GPUs

Sridhar

Vincenzi

Ruggiero

et al. 2012

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

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Abstract-With the rising challenges in heat removal in integrated circuits (ICs), the development of thermal-aware computing architectures and run-time management systems has become indispensable to the continuation of IC design scaling. These thermal-aware design technologies of the future strongly depend on the availability of efficient and accurate means for thermal modeling and analysis. These thermal models must have not only the sufficient accuracy to capture the complex mechanisms that regulate thermal diffusion in ICs, but also a level of abstraction that allows for their fast execution for design space exploration. In this paper, we propose an innovative thermal modeling approach for full-chips that can handle the scalability problem of transient heat flow simulation in large 2-D/3-D multiprocessor ICs. This is achieved by parallelizing the computation-intensive task of transient temperature tracking using neural networks and exploiting the computational power of massively parallel graphics processing units. Our results show up to 35× run-time speedup compared to state-of-the-art IC thermal simulation tools while keeping the error lower than 1°C. Speedups scale with the size of the 3-D multiprocessor ICs and our proposed method serves as a valuable design space exploration tool.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Neural Network-Based Thermal Simulation of Integrated Circuits on GPUs

Sridhar

Vincenzi

Ruggiero

et al. 2012

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

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“…We argue that run-time adaptability is a crucial parameter of interest. We present a run-time adaptable thermal simulator compatible with arbitrary sensor configuration based on the Neural Network (NN) simulator presented in [14]. We present experimental results on Niagara UltraSPARC T1 chip with real-life benchmark applications.…”

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confidence: 99%

Run-time adaptable on-chip thermal triggers

Kumar

Alonso

2011

16th Asia and South Pacific Design Automation Conference (ASP-DAC 2011)

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Abstract-With ever-increasing power densities, Dynamic Thermal Management (DTM) techniques have become mainstream in today's systems. An important component of such techniques is the thermal trigger. It has been shown that predictive thermal triggers can outperform reactive ones [4]. In this paper, we present a novel trade-off space of predictive thermal triggers, and compare different approaches proposed in the literature. We argue that run-time adaptability is a crucial parameter of interest. We present a run-time adaptable thermal simulator compatible with arbitrary sensor configuration based on the Neural Network (NN) simulator presented in [14]. We present experimental results on Niagara UltraSPARC T1 chip with real-life benchmark applications. Our results quantitatively establish the effectiveness of the proposed simulator for reducing (by up to 90%), the otherwise unacceptably high errors, that can arise due to expected leakage current variation and design-time thermal modeling errors.

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“…The work in [67] enhances runtime thermal management by providing an on-chip temperature predictor based on feedforward neural networks [46]. The analysis and mitigation of the impact of process variation undertaken in [60] are facilitated by a linear regression model [46] constructed based on measurements of static power with the goal of predicting peak temperatures.…”

Section: Previous Workmentioning

confidence: 99%

“…However, as noted in Section 7.1, the most recent advancements have not yet been sufficiently explored in this context. In particular, the utility of neural networks has been studied only marginally: feedforward neural networks-which are utilized, for instance, in [53,67]-are arguably the simplest and least powerful members of the family. However, the family is rich and potent.…”

Section: Previous Workmentioning

confidence: 99%

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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Neural network based on-chip thermal simulator

Cited by 13 publications

References 16 publications

Neural Network-Based Thermal Simulation of Integrated Circuits on GPUs

Neural Network-Based Thermal Simulation of Integrated Circuits on GPUs

Run-time adaptable on-chip thermal triggers

System-Level Analysis and Design under Uncertainty

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