Architectural support for probabilistic branches

A plethora of research efforts have focused on fine-tuning branch predictors to increasingly higher levels of accuracy. However, several important optimization, financial, and statistical data analysis algorithms rely on probabilistic computation. These applications draw random values from a distribution and steer control flow based on those values. Such probabilistic branches are challenging to predict because of their inherent probabilistic nature. As a result, probabilistic codes significantly suffer from branch mispredictions. This paper proposes Probabilistic Branch Support (PBS), a hardware/software cooperative technique that leverages the observation that the outcome of probabilistic branches needs to be correct only in a statistical sense. PBS stores the outcome and the probabilistic values that lead to the outcome of the current execution to direct the next execution of the probabilistic branch, thereby completely removing the penalty for mispredicted probabilistic branches. PBS relies on marking probabilistic branches in software for hardware to exploit. Our evaluation shows that PBS improves MPKI by 45% on average (and up to 99%) and IPC by 6.7% (up to 17%) over the TAGE-SC-L predictor. PBS requires 193 bytes of hardware overhead and introduces statistically negligible algorithmic inaccuracy

A compositional method for reliability analysis of workflows affected by multiple failure modes

We focus on reliability analysis for systems designed as workflow based compositions of components. Components are characterized by their failure profiles, which take into account possible multiple failure modes. A compositional calculus is provided to evaluate the failure profile of a composite system, given failure profiles of the components. The calculus is described as a syntax-driven procedure that synthesizes a workflows failure profile. The method is viewed as a design-time aid that can help software engineers reason about systems reliability in the early stage of development. A simple case study is presented to illustrate the proposed approach

Towards a Context Knowledge Taxonomy. Combined Methodologies to Improve a Fast-Search Concept Extraction for an Ontology Population

Context in Architectural Design can be defined-related-comparable to hypothesis and boundary conditions in mathematics. An eco-system that influences it by means of natural and artificial events, space and time dimension. The research has the aim to analyze the critical issues related to Context by providing a contribution to the study of interactions between Context Knowledge and Architectural Design and how it can be used to improve the performance of the buildings and reducing design mistakes. The research focusing on formal ontologies, has developed a model that enables a semantic approach to design application programs, to manage information, to answer design questions and to have a clear relation between the formal representation of the context domain and its meanings. This context model provides an advancement on the state of the art in simplified design assumptions, in term of ontology ambiguity and complexity reduction, by using algorithms to extract and optimize branches of the graph. The extraction does not limit the number of relations, that can be extended and improve context taxonomy coherency and accuracy

Hierarchical Deep Learning Architecture For 10K Objects Classification

Evolution of visual object recognition architectures based on Convolutional Neural Networks & Convolutional Deep Belief Networks paradigms has revolutionized artificial Vision Science. These architectures extract & learn the real world hierarchical visual features utilizing supervised & unsupervised learning approaches respectively. Both the approaches yet cannot scale up realistically to provide recognition for a very large number of objects as high as 10K. We propose a two level hierarchical deep learning architecture inspired by divide & conquer principle that decomposes the large scale recognition architecture into root & leaf level model architectures. Each of the root & leaf level models is trained exclusively to provide superior results than possible by any 1-level deep learning architecture prevalent today. The proposed architecture classifies objects in two steps. In the first step the root level model classifies the object in a high level category. In the second step, the leaf level recognition model for the recognized high level category is selected among all the leaf models. This leaf level model is presented with the same input object image which classifies it in a specific category. Also we propose a blend of leaf level models trained with either supervised or unsupervised learning approaches. Unsupervised learning is suitable whenever labelled data is scarce for the specific leaf level models. Currently the training of leaf level models is in progress; where we have trained 25 out of the total 47 leaf level models as of now. We have trained the leaf models with the best case top-5 error rate of 3.2% on the validation data set for the particular leaf models. Also we demonstrate that the validation error of the leaf level models saturates towards the above mentioned accuracy as the number of epochs are increased to more than sixty.Comment: As appeared in proceedings for CS & IT 2015 - Second International Conference on Computer Science & Engineering (CSEN 2015

Design of multimedia processor based on metric computation

Media-processing applications, such as signal processing, 2D and 3D graphics rendering, and image compression, are the dominant workloads in many embedded systems today. The real-time constraints of those media applications have taxing demands on today's processor performances with low cost, low power and reduced design delay. To satisfy those challenges, a fast and efficient strategy consists in upgrading a low cost general purpose processor core. This approach is based on the personalization of a general RISC processor core according the target multimedia application requirements. Thus, if the extra cost is justified, the general purpose processor GPP core can be enforced with instruction level coprocessors, coarse grain dedicated hardware, ad hoc memories or new GPP cores. In this way the final design solution is tailored to the application requirements. The proposed approach is based on three main steps: the first one is the analysis of the targeted application using efficient metrics. The second step is the selection of the appropriate architecture template according to the first step results and recommendations. The third step is the architecture generation. This approach is experimented using various image and video algorithms showing its feasibility

A synthesis of logic and biology in the design of dependable systems

The technologies of model-based design and dependability analysis in the design of dependable systems, including software intensive systems, have advanced in recent years. Much of this development can be attributed to the application of advances in formal logic and its application to fault forecasting and verification of systems. In parallel, work on bio-inspired technologies has shown potential for the evolutionary design of engineering systems via automated exploration of potentially large design spaces. We have not yet seen the emergence of a design paradigm that combines effectively and throughout the design lifecycle these two techniques which are schematically founded on the two pillars of formal logic and biology. Such a design paradigm would apply these techniques synergistically and systematically from the early stages of design to enable optimal refinement of new designs which can be driven effectively by dependability requirements. The paper sketches such a model-centric paradigm for the design of dependable systems that brings these technologies together to realise their combined potential benefits