DocumentCode :
177280
Title :
ArchRanker: A ranking approach to design space exploration
Author :
Tianshi Chen ; Qi Guo ; Ke Tang ; Temam, Olivier ; Zhiwei Xu ; Zhi-Hua Zhou ; Yunji Chen
Author_Institution :
State Key Lab. of Comput. Archit., Inst. of Comput. Technol. (ICT), Beijing, China
fYear :
2014
fDate :
14-18 June 2014
Firstpage :
85
Lastpage :
96
Abstract :
Architectural Design Space Exploration (DSE) is a notoriously difficult problem due to the exponentially large size of the design space and long simulation times. Previously, many studies proposed to formulate DSE as a regression problem which predicts architecture responses (e.g., time, power) of a given architectural configuration. Several of these techniques achieve high accuracy, though often at the cost of significant simulation time for training the regression models.We argue that the information the architect mostly needs during the DSEprocess is whether a given configuration will perform better than another one in the presences ofdesign constraints, or better than any other one seen so far, rather than precisely estimating the performance of that configuration. Based on this observation, we propose a novel rankingbased approach to DSE where we train a model to predict which of two architecture configurations will perform best. We show that, not only this ranking model more accurately predicts the relative merit of two architecture configurations than an ANN-based state-of-the-art regression model, but also that it requires much fewer training simulations to achieve the same accuracy, or that it can be used for and is even better at quantifying the performance gap between two configurations. We implement the framework for training and using this model, called ArchRanker, and we evaluate it on several DSE scenarios (unicore/multicore design spaces, and both time and power performance metrics). We try to emulate as closely as possible the DSE process by creating constraint-based scenarios, or an iterative DSEprocess. We find that ArchRanker makes 29.68% to 54.43% fewer incorrect predictions on pairwise relative merit of configurations (tested with 79,800 configuration pairs) than an ANN-based regression model across all DSE scenarios considered (values averaged over all benchmarks for each scenario). We also find that, to achieve the same accuracy as ArchRanke- , the ANN often requires three times more training simulations.
Keywords :
multiprocessing systems; neural net architecture; performance evaluation; regression analysis; ANN-based regression model; ANN-based state-of-the-art regression model; ArchRanker; architectural configuration; architectural design space exploration; architecture configuration; architecture response; constraint-based scenario; fdesign constraints; iterative DSE process; multicore design space; power performance metrics; ranking approach; regression problem; simulation time; training simulation; unicore design space; Accuracy; Benchmark testing; Hidden Markov models; Multicore processing; Predictive models; Training;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Architecture (ISCA), 2014 ACM/IEEE 41st International Symposium on
Conference_Location :
Minneapolis, MN
Print_ISBN :
978-1-4799-4396-8
Type :
conf
DOI :
10.1109/ISCA.2014.6853198
Filename :
6853198
Link To Document :
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