Transient Temperature Prediction for Aging Thermal Sensors Using Artificial Neural Network
| Autor: | Kameswar Rao Vaddina, Juan M. Cebrian, Lasse Natvig |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
010302 applied physics
Negative-bias temperature instability Artificial neural network Computer science Hardware_PERFORMANCEANDRELIABILITY 02 engineering and technology Propagation delay 01 natural sciences Temperature measurement Electromigration 020202 computer hardware & architecture 0103 physical sciences Scalability Hardware_INTEGRATEDCIRCUITS 0202 electrical engineering electronic engineering information engineering Electronic engineering Cluster analysis Hot-carrier injection |
| Zdroj: | PDP |
| DOI: | 10.1109/pdp.2016.89 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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