An algorithmic and architectural study on Montgomery exponentiation in RNS

The modular exponentiation on large numbers is computationally intensive. An effective way for performing this operation consists in using Montgomery exponentiation in the Residue Number System (RNS). This paper presents an algorithmic and architectural study of such exponentiation approach. From the algorithmic point of view, new and state-of-the-art opportunities that come from the reorganization of operations and precomputations are considered. From the architectural perspective, the design opportunities offered by well-known computer arithmetic techniques are studied, with the aim of developing an efficient arithmetic cell architecture. Furthermore, since the use of efficient RNS bases with a low Hamming weight are being considered with ever more interest, four additional cell architectures specifically tailored to these bases are developed and the tradeoff between benefits and drawbacks is carefully explored. An overall comparison among all the considered algorithmic approaches and cell architectures is presented, with the aim of providing the reader with an extensive overview of the Montgomery exponentiation opportunities in RNS

ART Neural Networks: Distributed Coding and ARTMAP Applications

ART (Adaptive Resonance Theory) neural networks for fast, stable learning and prediction have been applied in a variety of areas. Applications include airplane design and manufacturing, automatic target recognition, financial forecasting, machine tool monitoring, digital circuit design, chemical analysis, and robot vision. Supervised ART architectures, called ARTMAP systems, feature internal control mechanisms that create stable recognition categories of optimal size by maximizing code compression while minimizing predictive error in an on-line setting. Special-purpose requirements of various application domains have led to a number of ARTMAP variants, including fuzzy ARTMAP, ART-EMAP, Gaussian ARTMAP, and distributed ARTMAP. ARTMAP has been used for a variety of applications, including computer-assisted medical diagnosis. Medical databases present many of the challenges found in general information management settings where speed, efficiency, ease of use, and accuracy are at a premium. A direct goal of improved computer-assisted medicine is to help deliver quality emergency care in situations that may be less than ideal. Working with these problems has stimulated a number of ART architecture developments, including ARTMAP-IC [1]. This paper describes a recent collaborative effort, using a new cardiac care database for system development, has brought together medical statisticians and clinicians at the New England Medical Center with researchers developing expert systems and neural networks, in order to create a hybrid method for medical diagnosis. The paper also considers new neural network architectures, including distributed ART {dART), a real-time model of parallel distributed pattern learning that permits fast as well as slow adaptation, without catastrophic forgetting. Local synaptic computations in the dART model quantitatively match the paradoxical phenomenon of Markram-Tsodyks [2] redistribution of synaptic efficacy, as a consequence of global system hypotheses.Office of Naval Research (N00014-95-1-0409, N00014-95-1-0657

High-Performance Computing for Scheduling Decision Support: A Parallel Depth-First Search Heuristic

Many academic disciplines - including information systems, computer science, and operations management - face scheduling problems as important decision making tasks. Since many scheduling problems are NP-hard in the strong sense, there is a need for developing solution heuristics. For scheduling problems with setup times on unrelated parallel machines, there is limited research on solution methods and to the best of our knowledge, parallel computer architectures have not yet been taken advantage of. We address this gap by proposing and implementing a new solution heuristic and by testing different parallelization strategies. In our computational experiments, we show that our heuristic calculates near-optimal solutions even for large instances and that computing time can be reduced substantially by our parallelization approach

Next Generation Cloud Computing: New Trends and Research Directions

The landscape of cloud computing has significantly changed over the last decade. Not only have more providers and service offerings crowded the space, but also cloud infrastructure that was traditionally limited to single provider data centers is now evolving. In this paper, we firstly discuss the changing cloud infrastructure and consider the use of infrastructure from multiple providers and the benefit of decentralising computing away from data centers. These trends have resulted in the need for a variety of new computing architectures that will be offered by future cloud infrastructure. These architectures are anticipated to impact areas, such as connecting people and devices, data-intensive computing, the service space and self-learning systems. Finally, we lay out a roadmap of challenges that will need to be addressed for realising the potential of next generation cloud systems.Comment: Accepted to Future Generation Computer Systems, 07 September 201

Використання структур сучасних комп’ютерних систем для реалізації систем обробки знань

У зв’язку зі стрімким розвитком технологій програмованих логічних інтегральних схем розробка нових, більш продуктивних архітектур комп’ютерів для обробки знань залишається актуальною задачею. В роботі виконано аналіз архітектур проблемно-орієнтованих систем, а також універсальних систем. Розглянуто переваги та шляхи використання комп’ютерів із універсальною архітектурою для реалізації систем обробки знань.В связи со стремительным ростом технологий программируемых логических инте- гральных схем разработка новых, более производительных архитектур компьютеров для обработки знаний остается актуальной задачей. В работе выполнен анализ архитектур проблемноориентированных и универсальных компьютерных систем. Рассмотрены преимущества и пути использования компьютеров с универсальной архитектурой для реализации систем обработки знаний.Due to the rapid technologies growth of programmable logic integrated circuits, developing of a new computer’s architectures, which effectively support knowledge processing systems, remains a relevant problem. The analysis of task-oriented architectures and universal computer systems was conducted. The advantages and ways of using computers with a universal architecture for knowledge-processing systems implementation were considered

High-Performance Deep learning to Detection and Tracking Tomato Plant Leaf Predict Disease and Expert Systems

Nowadays, technology and computer science are rapidly developing many tools and algorithms, especially in the field of artificial intelligence. Machine learning is involved in the development of new methodologies and models that have become a novel machine learning area of applications for artificial intelligence. In addition to the architectures of conventional neural network methodologies, deep learning refers to the use of artificial neural network architectures which include multiple processing layers./nIn this paper, models of the Convolutional neural network were designed to detect (diagnose) plant disorders by applying samples of healthy and unhealthy plant images analyzed by means of methods of deep learning. The models were trained using an open data set containing (18,000) images of ten different plants, including healthy plants. Several model architectures have been trained to achieve the best performance of (97 percent) when the respectively [plant, disease] paired are detected. This is a very useful information or early warning technique and a method that can be further improved with the substantially high-performance rate to support an automated plant disease detection system to work in actual farm conditions

Developing and Evaluating Cognitive Architectures with Behavioural Tests

http://www.aaai.org/Press/Reports/Workshops/ws-07-04.phpWe present a methodology for developing and evaluating cognitive architectures based on behavioural tests and suitable optimisation algorithms. Behavioural tests are used to clarify those aspects of an architecture's implementation which are critical to that theory. By fitting the performance of the architecture to observed behaviour, values for the architecture's parameters can be automatically obtained, and information can be derived about how components of the architecture relate to performance. Finally, with an appropriate optimisation algorithm, different cognitive architectures can be evaluated, and their performances compared on multiple tasks.Peer reviewe