913 research outputs found

    Assisted assignment of automotive safety requirements

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    ISO 26262, a functional-safety standard, uses Automotive Safety Integrity Levels (ASILs) to assign safety requirements to automotive-system elements. System designers initially assign ASILs to system-level hazards and then allocate them to elements of the refined system architecture. Through ASIL decomposition, designers can divide a function & rsquo;s safety requirements among multiple components. However, in practice, manual ASIL decomposition is difficult and produces varying results. To overcome this problem, a new tool automates ASIL allocation and decomposition. It supports the system and software engineering life cycle by enabling users to efficiently allocate safety requirements regarding systematic failures in the design of critical embedded computer systems. The tool is applicable to industries with a similar concept of safety integrity levels. © 1984-2012 IEEE

    Ant-based Neural Topology Search (ANTS) for Optimizing Recurrent Networks

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    Hand-crafting effective and efficient structures for recurrent neural networks (RNNs) is a difficult, expensive, and time-consuming process. To address this challenge, we propose a novel neuro-evolution algorithm based on ant colony optimization (ACO), called Ant-based Neural Topology Search (ANTS), for directly optimizing RNN topologies. The procedure selects from multiple modern recurrent cell types such as ∆-RNN, GRU, LSTM, MGU and UGRNN cells, as well as recurrent connections which may span multiple layers and/or steps of time. In order to introduce an inductive bias that encourages the formation of sparser synaptic connectivity patterns, we investigate several variations of the core algorithm. We do so primarily by formulating different functions that drive the underlying pheromone simulation process (which mimic L1 and L2 regularization in standard machine learning) as well as by introducing ant agents with specialized roles (inspired by how real ant colonies operate), i.e., explorer ants that construct the initial feed forward structure and social ants which select nodes from the feed forward connections to subsequently craft recurrent memory structures. We also incorporate communal intelligence, where best weights are shared by the ant colony for weight initialization, reducing the number of backpropagation epochs required to locally train candidate RNNs, speeding up the neuro-evolution process. Our results demonstrate that the sparser RNNs evolved by ANTS significantly outperform traditional one and two layer architectures consisting of modern memory cells, as well as the well-known NEAT algorithm. Furthermore, we improve upon prior state-of-the-art results on the time series dataset utilized in our experiments

    Automatic synthesis and optimization of chip multiprocessors

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    The microprocessor technology has experienced an enormous growth during the last decades. Rapid downscale of the CMOS technology has led to higher operating frequencies and performance densities, facing the fundamental issue of power dissipation. Chip Multiprocessors (CMPs) have become the latest paradigm to improve the power-performance efficiency of computing systems by exploiting the parallelism inherent in applications. Industrial and prototype implementations have already demonstrated the benefits achieved by CMPs with hundreds of cores.CMP architects are challenged to take many complex design decisions. Only a few of them are:- What should be the ratio between the core and cache areas on a chip?- Which core architectures to select?- How many cache levels should the memory subsystem have?- Which interconnect topologies provide efficient on-chip communication?These and many other aspects create a complex multidimensional space for architectural exploration. Design Automation tools become essential to make the architectural exploration feasible under the hard time-to-market constraints. The exploration methods have to be efficient and scalable to handle future generation on-chip architectures with hundreds or thousands of cores.Furthermore, once a CMP has been fabricated, the need for efficient deployment of the many-core processor arises. Intelligent techniques for task mapping and scheduling onto CMPs are necessary to guarantee the full usage of the benefits brought by the many-core technology. These techniques have to consider the peculiarities of the modern architectures, such as availability of enhanced power saving techniques and presence of complex memory hierarchies.This thesis has several objectives. The first objective is to elaborate the methods for efficient analytical modeling and architectural design space exploration of CMPs. The efficiency is achieved by using analytical models instead of simulation, and replacing the exhaustive exploration with an intelligent search strategy. Additionally, these methods incorporate high-level models for physical planning. The related contributions are described in Chapters 3, 4 and 5 of the document.The second objective of this work is to propose a scalable task mapping algorithm onto general-purpose CMPs with power management techniques, for efficient deployment of many-core systems. This contribution is explained in Chapter 6 of this document.Finally, the third objective of this thesis is to address the issues of the on-chip interconnect design and exploration, by developing a model for simultaneous topology customization and deadlock-free routing in Networks-on-Chip. The developed methodology can be applied to various classes of the on-chip systems, ranging from general-purpose chip multiprocessors to application-specific solutions. Chapter 7 describes the proposed model.The presented methods have been thoroughly tested experimentally and the results are described in this dissertation. At the end of the document several possible directions for the future research are proposed

    Green Parallel Metaheuristics: Design, Implementation, and Evaluation

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    Fecha de lectura de Tesis Doctoral 14 mayo 2020Green parallel metaheuristics (GPM) is a new concept we want to introduce in this thesis. It is an idea inspired by two facts: (i) parallel metaheuristics could help as unique tools to solve optimization problems in energy savings applications and sustainability, and (ii) these algorithms themselves run on multiprocessors, clusters, and grids of computers and then consume energy, so they need an energy analysis study for their different implementations over multiprocessors. The context for this thesis is to make a modern and competitive effort to extend the capability of present intelligent search optimization techniques. Analyzing the different sequential and parallel metaheuristics considering its energy consumption requires a deep investigation of the numerical performance, the execution time for efficient future designing to these algorithms. We present a study of the speed-up of the different parallel implementations over a different number of computing units. Moreover, we analyze and compare the energy consumption and numerical performance of the sequential/parallel algorithms and their components: a jump in the efficiency of the algorithms that would probably have a wide impact on the domains involved.El Instituto Egipcio en Madrid, dependiente del Gobierno de Egipto

    Derivation of Power System Module Metamodels for Early Shipboard Design Explorations

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    The U.S. Navy is currently challenged to develop new ship designs under compressed schedules. These ship designs must necessarily incorporate emerging technologies for high power energy conversion in order to enable smaller ship designs with a high degree of electrification and next generation electrified weapons. One way this challenge is being addressed is through development of collaborative concurrent design environment that allows for design space exploration across a wide range of implementation options. The most significant challenge is assurance of a dependable power and energy service via the shipboard Integrated Power and Energy System (IPES). The IPES is largely made up of interconnected power conversion and distribution equipment with allocated functionalities in order to meet demanding Quality of Power, Quality of Service and Survivability requirements. Feasible IPES implementations must fit within the ship hull constraints and must not violate limitations on ship displacement. This Thesis applies the theory of dependability to the use of scalable metamodels for power conversion and distribution equipment within a collaborative concurrent design environment to enable total ship set-based design outcomes that result implementable design specifications for procurement of equipment to be used in the final ship implementation

    Derivation of Power System Module Metamodels for Early Shipboard Design Explorations

    Get PDF
    The U.S. Navy is currently challenged to develop new ship designs under compressed schedules. These ship designs must necessarily incorporate emerging technologies for high power energy conversion in order to enable smaller ship designs with a high degree of electrification and next generation electrified weapons. One way this challenge is being addressed is through development of collaborative concurrent design environment that allows for design space exploration across a wide range of implementation options. The most significant challenge is assurance of a dependable power and energy service via the shipboard Integrated Power and Energy System (IPES). The IPES is largely made up of interconnected power conversion and distribution equipment with allocated functionalities in order to meet demanding Quality of Power, Quality of Service and Survivability requirements. Feasible IPES implementations must fit within the ship hull constraints and must not violate limitations on ship displacement. This Thesis applies the theory of dependability to the use of scalable metamodels for power conversion and distribution equipment within a collaborative concurrent design environment to enable total ship set-based design outcomes that result implementable design specifications for procurement of equipment to be used in the final ship implementation

    Pulse shape optimization for electron-positron production in rotating fields

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    We optimize the pulse shape and polarization of time-dependent electric fields to maximize the production of electron-positron pairs via strong field quantum electrodynamics processes. The pulse is parametrized in Fourier space by a B-spline polynomial basis, which results in a relatively low-dimensional parameter space while still allowing for a large number of electric field modes. The optimization is performed by using a parallel implementation of the differential evolution, one of the most efficient metaheuristic algorithms. The computational performance of the numerical method and the results on pair production are compared with a local multistart optimization algorithm. These techniques allow us to determine the pulse shape and field polarization that maximize the number of produced pairs in computationally accessible regimes.Comment: 16 pages, 10 figure
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