No abstract

No abstract

No abstract

Abstract. During development, processor architectures can be tuned and configured by many different parameters. For benchmarking, automatic design space explorations (DSEs) with heuristic algorithms are a helpful approach to find the best settings for these parameters according to multiple objectives, e.g. performance, energy consumption, or real-time constraints. But if the setup is slightly changed and a new DSE has to be performed, it will start from scratch, resulting in very long evaluation times.To reduce the evaluation times we extend the NSGA-II algorithm in this article, such that automatic DSEs can be supported with a set of transformation rules defined in a highly readable format, the fuzzy control language (FCL). Rules can be specified by an engineer, thereby representing existing knowledge. Beyond this, a decision tree classifying high-quality configurations can be constructed automatically and translated into transformation rules. These can also be seen as very valuable result of a DSE because they allow drawing conclusions on the influence of parameters and describe regions of the design space with high density of good configurations.Our evaluations show that automatically generated decision trees can classify near optimal configurations for the hardware parameters of the Grid ALU Processor (GAP) and M-Sim 2. Further evaluations show that automatically constructed transformation rules can reduce the number of evaluations required to reach the same quality of results as without rules by 43%, leading to a significant saving of time of about 25%. In the demonstrated example using rules also leads to better results.

Abstract. In today's computer architectures the design spaces are huge, thus making it very difficult to find optimal configurations. One way to cope with this problem is to use Automatic Design Space Exploration (ADSE) techniques. We developed the Framework for Automatic Design Space Exploration (FADSE) which is focused on microarchitectural optimizations. This framework includes several state-of-the art heuristic algorithms. In this paper we selected three of them, NSGA-II and SPEA2 as genetic algorithms as well as SMPSO as a particle swarm optimization, and compared their performance. As test case we optimize the parameters of the Grid ALU Processor (GAP) microarchitecture and then GAP together with the post-link code optimizer GAPtimize. An analysis of the simulation results shows a very good performance of all the three algorithms. SMPSO reveals the fastest convergence speed. A clear winner between NSGA-II and SPEA2 cannot be determined.

The majority of currently available branch predictors base their prediction accuracy on the previous k branch outcomes. Such predictors sustain high prediction accuracy but they do not consider the impact of unbiased branches which are difficult-to-predict. In this paper, we quantify and evaluate the impact of unbiased branches and show that any gain in prediction accuracy is proportional to the frequency of unbiased branches. By using the SPECcpu2000 integer benchmarks we show that there are a significant proportion of unbiased branches which severely impact on prediction accuracy (averaging between 6% and 24% depending on the prediction context used).

Text categorization is the problem of classifying text documents into a set of predefined classes. In this paper, we investigated two approaches: a) to develop a classifier for text document based on Naive Bayes Theory and b) to integrate this classifier into a meta-classifier in order to increase the classification accuracy. The basic idea is to learn a meta-classifier to optimally select the best component classifier for each data point. The experimental results show that combining classifiers can significantly improve the classification accuracy and that our improved meta-classification strategy gives better results than each individual classifier. For Reuters2000 text documents we obtained classification accuracies up to 93.87%.

No abstract