A Dimensionless Graceful Degradation Metric for Quantifying Resilience

Beal, Jacob

doi:10.1109/sasow.2012.24

Cited by 1 publication

(1 citation statement)

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“…The average execution time, in terms of clock cycles and for the different numbers of faults, is reported in Figure 16 for the block and in Figure 17 for the bypass. Even though this can only provide raw indications, it should be considered the intrinsic complexity of the evaluation of the graceful degradation features in distributed-control systems with autonomic behaviors [Beal 2012].…”

Section: Cell-exclusion Impact On the Processing Latencymentioning

confidence: 99%

Computing Swarms for Self-Adaptiveness and Self-Organization in Floating-Point Array Processing

Pani

Palumbo

et al. 2015

ACM Trans. Auton. Adapt. Syst.

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Advancements in CMOS technology enable the integration of a huge number of resources on the same systemon-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.

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Section: Cell-exclusion Impact On the Processing Latencymentioning

confidence: 99%