Towards A Semiformal Development Methodology for Embedded Systems

In recent days, the amount of functions has increased significantly in embedded products so that systems development methodologies play an important role to ensure the product’s quality, cost, and time. Furthermore, this complexity coupled with constantly evolving specifications, has led to propose a semiformal development methodology to support the building of embedded real-time systems. A platform-based design approach has been used to balance costs and time-to-market in relation to performance and functionality constraints. We performed three expressive case studies and we concluded that the proposed methodology significantly reduces design time and improves software modularity and reliability

Optimizing Neural Architecture Search using Limited GPU Time in a Dynamic Search Space: A Gene Expression Programming Approach

Efficient identification of people and objects, segmentation of regions of interest and extraction of relevant data in images, texts, audios and videos are evolving considerably in these past years, which deep learning methods, combined with recent improvements in computational resources, contributed greatly for this achievement. Although its outstanding potential, development of efficient architectures and modules requires expert knowledge and amount of resource time available. In this paper, we propose an evolutionary-based neural architecture search approach for efficient discovery of convolutional models in a dynamic search space, within only 24 GPU hours. With its efficient search environment and phenotype representation, Gene Expression Programming is adapted for network's cell generation. Despite having limited GPU resource time and broad search space, our proposal achieved similar state-of-the-art to manually-designed convolutional networks and also NAS-generated ones, even beating similar constrained evolutionary-based NAS works. The best cells in different runs achieved stable results, with a mean error of 2.82% in CIFAR-10 dataset (which the best model achieved an error of 2.67%) and 18.83% for CIFAR-100 (best model with 18.16%). For ImageNet in the mobile setting, our best model achieved top-1 and top-5 errors of 29.51% and 10.37%, respectively. Although evolutionary-based NAS works were reported to require a considerable amount of GPU time for architecture search, our approach obtained promising results in little time, encouraging further experiments in evolutionary-based NAS, for search and network representation improvements.Comment: Accepted for presentation at the IEEE Congress on Evolutionary Computation (IEEE CEC) 202

On-site approximation for spin-orbit coupling in LCAO density functional methods

We propose a computational method that simplifies drastically the inclusion of spin-orbit interaction in density functional theory implemented on localised atomic orbital basis sets. Our method is based on a well-known procedure for obtaining pseudopotentials from atomic relativistic 'ab initio' calculations and on an on-site approximation for the spin-orbit matrix elements. We have implemented the technique in the SIESTA code, and we show that it provides accurate results for the overall band structure and splittings of group IV and III-IV semiconductors as well as for 5d metals.Comment: 8 pages, 4 figures. Published in J. Phys.: Condens. Matter 18 7999-8013, 2006. Some errata correcte