Due to the increasing complexity of the processor architecture and the time-consuming software simulation, efficient design space exploration (DSE) has become a critical challenge in processor design. To address this challenge, recently machine learning techniques have been widely explored for predicting the performance of various configurations through conducting only a small number of simulations as the training samples. However, most existing methods randomly select some samples for simulation from the entire configuration space as training samples to build program-specific predictors. When a new program is considered, a large number of new program-specific simulations are needed for building a new predictor. Thus considerable simulation cost is required for each program. In this paper, we propose an efficient cross-program DSE framework TrEE by combining a flexible statistical sampling strategy and ensemble transfer learning technique. Specifically, TrEE includes the following two phases which also form our major contributions: 1) proposing an orthogonal array based foldover design for flexibly sampling the representative configurations for simulation, and 2) proposing an ensemble transfer learning algorithm that can effectively transfer knowledge among different types of programs for improving the prediction performance for the new program. We evaluate the proposed TrEE on the benchmarks of SPEC CPU 2006 suite. The results demonstrate that TrEE is much more efficient and robust than state-of-art DSE techniques.

Design space exploration (DSE) has become a notoriously difficult problem due to the exponentially increasing size of design space of microprocessors and time-consuming simulations. To address this issue, machine learning techniques have been widely employed to build predictive models. However, most previous approaches randomly sample the training set leading to considerable simulation cost and low prediction accuracy. In this paper, we propose an efficient and precise DSE methodology by combining statistical sampling and Adaboost learning technique. The proposed method includes three phases. (1) Firstly, orthogonal design based feature selection is employed to prune design space. (2) Sencondly, an orthogonal array based training data sampling method is introduced to select the representative configurations for simulation. (3) Finally, a new active learning approach ActBoost is proposed to build predictive model. Evaluations demonstrate that the proposed framework is more efficient and precise than state-of-art DSE techniques.

The aim of the paper is to develop a methodology for reliability data estimation of services, and to develop a model to select the most suitable set of services for service composition, to support the reliability requirement of building a SOA system. The proposed methodology involves qualitative modeling of service composition using Stochastic Petri net (SPN) and quantitative analysis using fuzzy set theory for representing the failure rates and repair rates. Failure rates and repair rates are incorporated into SPN model and system reliability parameters are computed. A multi-objective genetic algorithm (GA) is adopted to select the most appropriate set of services using reliability parameters such as availability, reliability, and MTBF as the criterion of interest. The adopted methodology gives a graphical representation of the structure of the system, and selects the most reliable services. An example of a travel process has been performed to demonstrate the proposed method. The results may be helpful for the designers to assess the reliability data of the system and to select best services for building the more reliable SOA system.