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INVITED: Cross-Layer Modeling and Optimization for Electromigration Induced Reliability.

Authors :
Taeyoung Kim
Zeyu Sun
Chase Cook
Hengyang Zhao
Ruiwen Li
Daniel Wong
Tan, Sheldon X.-D.
Source :
DAC: Annual ACM/IEEE Design Automation Conference; Jun2016, p174-179, 6p
Publication Year :
2016

Abstract

In this paper, we propose a new approach for cross-layer electromigration (EM) induced reliability modeling and optimization at physics, system and datacenter levels. We consider a recently proposed physics-based electromigration (EM) reliability model to predict the EM reliability of full-chip power grid networks for long-term failures. We show how the new physics-based dynamic EM model at the physics level can be abstracted at the system level and even at the datacenter level. Our datacenter system-level power model is based on the BigHouse simulator. To speed up the online optimization for energy in a datacenter, we propose a new combined datacenter power and reliability compact model using a learning based approach in which a feed-forward neural network (FNN) is trained to predict energy and long term reliability for each processor under datacenter scheduling and workloads. To optimize the energy and reliability of a datacenter, we apply the efficient adaptive Q-learning based reinforcement learning method. Experimental results show that the proposed compact models for the datacenter system trained with different workloads under different cluster power modes and scheduling policies are able to build accurate energy and lifetime. Moreover, the proposed optimization method effectively manages and optimizes data-center energy subject to reliability, given power budget and performance. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
0738100X
Database :
Complementary Index
Journal :
DAC: Annual ACM/IEEE Design Automation Conference
Publication Type :
Conference
Accession number :
116210522
Full Text :
https://doi.org/10.1145/2897937.2905010