The Experts below are selected from a list of 24 Experts worldwide ranked by ideXlab platform

Shen Xu - One of the best experts on this subject based on the ideXlab platform.

Luigi Raffo - One of the best experts on this subject based on the ideXlab platform.

  • Computing Swarms for Self-Adaptiveness and Self-Organization in Floating-Point Array Processing
    ACM Transactions on Autonomous and Adaptive Systems, 2015
    Co-Authors: Danilo Pani, Carlo Sau, Francesca Palumbo, Luigi Raffo
    Abstract:

    Advancements in CMOS technology enable the integration of a huge number of resources on the same system-on-chip. Managing the consequent growing complexity, including fault tolerance issues in deep submicron technologies, is a hard challenge for hardware designers. Self-organization may represent a viable path toward the development of massively parallel architectures in current and future technologies. This approach is progressively more studied in multiprocessor architectures where, however, a further mind-set shift in terms of programming paradigm is required. In this article, self-organization and self-adaptiveness are exploited for the design of a coprocessing unit for Array computations, supporting Floating-Point arithmetic. From the experience of previous explorations, an architecture embodying some principle of swarm intelligence to pursue adaptability, scalability, and fault tolerance is proposed. The architecture realizes a loosely structured collection of hardware agents implementing fixed behavioral rules aimed at the best exploitation of the available resources in whatever kind of context without any hardware reconfiguration. Comparisons with off-the-shelf very long instruction word (VLIW) digital signal processors (DSPs) on specific tasks reveal similar performance thus not paying the improved robustness with performance. The multitasking capabilities, together with the intrinsic scalability, make this approach valuable for future extensions as well, especially in the field of neuronal networks simulators.

  • DASIP - A nature-inspired adaptive Floating-Point coprocessing system
    2012
    Co-Authors: Danilo Pani, Francesca Palumbo, Luigi Raffo
    Abstract:

    On the wave of technology scaling, digital designers are quickly approaching the limits of current technologies. At the same time, deep submicron architectures are becoming more prone to transient errors and permanent faults. Swarm intelligence represents an interesting source of inspiration already used in the past for the design of decentralized-control hardware architectures with intrinsic properties of scalability, adaptability and fault tolerance, with a configuration-free approach. This paper presents the first swarm coprocessor for Floating Point Array processing with native multitasking support. The coprocessor, designed around a nature-inspired processing fabric, has been integrated on a Virtex6 FPGA with a MicroBlaze host processor and preliminarily evaluated on common applications. The performance of the system, joined to its fault tolerance support, reveals the potentialities of the approach opening to architectural improvements and mapping of safety-critical digital signal processing applications.

Danilo Pani - One of the best experts on this subject based on the ideXlab platform.

  • Computing Swarms for Self-Adaptiveness and Self-Organization in Floating-Point Array Processing
    ACM Transactions on Autonomous and Adaptive Systems, 2015
    Co-Authors: Danilo Pani, Carlo Sau, Francesca Palumbo, Luigi Raffo
    Abstract:

    Advancements in CMOS technology enable the integration of a huge number of resources on the same system-on-chip. Managing the consequent growing complexity, including fault tolerance issues in deep submicron technologies, is a hard challenge for hardware designers. Self-organization may represent a viable path toward the development of massively parallel architectures in current and future technologies. This approach is progressively more studied in multiprocessor architectures where, however, a further mind-set shift in terms of programming paradigm is required. In this article, self-organization and self-adaptiveness are exploited for the design of a coprocessing unit for Array computations, supporting Floating-Point arithmetic. From the experience of previous explorations, an architecture embodying some principle of swarm intelligence to pursue adaptability, scalability, and fault tolerance is proposed. The architecture realizes a loosely structured collection of hardware agents implementing fixed behavioral rules aimed at the best exploitation of the available resources in whatever kind of context without any hardware reconfiguration. Comparisons with off-the-shelf very long instruction word (VLIW) digital signal processors (DSPs) on specific tasks reveal similar performance thus not paying the improved robustness with performance. The multitasking capabilities, together with the intrinsic scalability, make this approach valuable for future extensions as well, especially in the field of neuronal networks simulators.

  • DASIP - A nature-inspired adaptive Floating-Point coprocessing system
    2012
    Co-Authors: Danilo Pani, Francesca Palumbo, Luigi Raffo
    Abstract:

    On the wave of technology scaling, digital designers are quickly approaching the limits of current technologies. At the same time, deep submicron architectures are becoming more prone to transient errors and permanent faults. Swarm intelligence represents an interesting source of inspiration already used in the past for the design of decentralized-control hardware architectures with intrinsic properties of scalability, adaptability and fault tolerance, with a configuration-free approach. This paper presents the first swarm coprocessor for Floating Point Array processing with native multitasking support. The coprocessor, designed around a nature-inspired processing fabric, has been integrated on a Virtex6 FPGA with a MicroBlaze host processor and preliminarily evaluated on common applications. The performance of the system, joined to its fault tolerance support, reveals the potentialities of the approach opening to architectural improvements and mapping of safety-critical digital signal processing applications.

Francesca Palumbo - One of the best experts on this subject based on the ideXlab platform.

  • Computing Swarms for Self-Adaptiveness and Self-Organization in Floating-Point Array Processing
    ACM Transactions on Autonomous and Adaptive Systems, 2015
    Co-Authors: Danilo Pani, Carlo Sau, Francesca Palumbo, Luigi Raffo
    Abstract:

    Advancements in CMOS technology enable the integration of a huge number of resources on the same system-on-chip. Managing the consequent growing complexity, including fault tolerance issues in deep submicron technologies, is a hard challenge for hardware designers. Self-organization may represent a viable path toward the development of massively parallel architectures in current and future technologies. This approach is progressively more studied in multiprocessor architectures where, however, a further mind-set shift in terms of programming paradigm is required. In this article, self-organization and self-adaptiveness are exploited for the design of a coprocessing unit for Array computations, supporting Floating-Point arithmetic. From the experience of previous explorations, an architecture embodying some principle of swarm intelligence to pursue adaptability, scalability, and fault tolerance is proposed. The architecture realizes a loosely structured collection of hardware agents implementing fixed behavioral rules aimed at the best exploitation of the available resources in whatever kind of context without any hardware reconfiguration. Comparisons with off-the-shelf very long instruction word (VLIW) digital signal processors (DSPs) on specific tasks reveal similar performance thus not paying the improved robustness with performance. The multitasking capabilities, together with the intrinsic scalability, make this approach valuable for future extensions as well, especially in the field of neuronal networks simulators.

  • DASIP - A nature-inspired adaptive Floating-Point coprocessing system
    2012
    Co-Authors: Danilo Pani, Francesca Palumbo, Luigi Raffo
    Abstract:

    On the wave of technology scaling, digital designers are quickly approaching the limits of current technologies. At the same time, deep submicron architectures are becoming more prone to transient errors and permanent faults. Swarm intelligence represents an interesting source of inspiration already used in the past for the design of decentralized-control hardware architectures with intrinsic properties of scalability, adaptability and fault tolerance, with a configuration-free approach. This paper presents the first swarm coprocessor for Floating Point Array processing with native multitasking support. The coprocessor, designed around a nature-inspired processing fabric, has been integrated on a Virtex6 FPGA with a MicroBlaze host processor and preliminarily evaluated on common applications. The performance of the system, joined to its fault tolerance support, reveals the potentialities of the approach opening to architectural improvements and mapping of safety-critical digital signal processing applications.

Hing C Chan - One of the best experts on this subject based on the ideXlab platform.

  • Software Development and Analysis for Experimental High Frequency Surface Wave Radar Data
    1992
    Co-Authors: Hing C Chan
    Abstract:

    Abstract : Processing software for data collected in an experimental High Frequency Surface Wave Radar (HFSWR) project is implemented in the VAX-3800 computer and the FPS-5000 Floating Point Array process. The software is used to process the data in-house so as to obtain insights in HFSWR sea clutter, noise and target characteristics,. The data are examined to assess quality. Several deficiencies are observed in the data, including 60 Hz power source harmonic interferences and discontinuities in data sequences. Preliminary analysis of some of the data shows that conventional Doppler processing of the data permits the detection of ship and large aircraft targets at fairly long ranges. Plans for further analysis of these data are discussed.