Title | ||
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Transient Temperature Prediction for Aging Thermal Sensors Using Artificial Neural Network |
Abstract | ||
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As technology scales down and power density increases, the temperature sensor characteristics will drift, leading to temperature errors which increase over time. Transistor aging is one of the leading contributors to temperature sensing inaccuracies. The prominent aging failure mechanisms like Negative Bias Temperature Instability (NBTI), Hot Carrier Injection (HCI) and electromigration have emerged as the main sources of system unreliability which manifest as an increase in the propagation delay over time. On-chip thermal sensors are not immune to this phenomenon and get affected by these aging mechanisms. Thermal sensor aging exacerbated by increased temperatures leads to temperature sensing inaccuracies requiring repeated sensor calibration. In this work, we propose a novel approach of using performance metrics to predict the transient temperature profile of an application as seen by the aging thermal sensor. Firstly, we make offline profiling of applications and then cluster them into groups using k-means clustering mechanism. Then we use a neural network model to predict the thermal profile of a new application given its performance metrics. The forecasting ability of our model is accessed using MSE and RMSE. This approach is highly scalable and can be used to predict future temperatures which can then be used for run-time dynamic thermal management of multi-core systems. |
Year | DOI | Venue |
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2016 | 10.1109/PDP.2016.89 | 2016 24th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP) |
Keywords | Field | DocType |
Temperature prediction,aging thermal sensors,NBTI,HCI,thermal characterization of applications,artificial neural networks,MPSoCs | Propagation delay,Computer science,Electronic engineering,Hot-carrier injection,Negative-bias temperature instability,Cluster analysis,Artificial neural network,Electromigration,Temperature measurement,Distributed computing,Scalability | Conference |
ISSN | Citations | PageRank |
1066-6192 | 0 | 0.34 |
References | Authors | |
13 | 3 |
Name | Order | Citations | PageRank |
---|---|---|---|
Kameswar Rao Vaddina | 1 | 73 | 8.05 |
Juan Manuel Cebrian | 2 | 24 | 10.19 |
Lasse Natvig | 3 | 109 | 19.61 |