Generic low-latency NoC router architecture for FPGA computing systems

A novel cost-effective and low-latency wormhole router for packet-switched NoC designs, tailored for FPGA, is presented. This has been designed to be scalable at system level to fully exploit the characteristics and constraints of FPGA based systems, rather than custom ASIC technology. A key feature is that it achieves a low packet propagation latency of only two cycles per hop including both router pipeline delay and link traversal delay - a significant enhancement over existing FPGA designs - whilst being very competitive in terms of performance and hardware complexity. It can also be configured in various network topologies including 1-D, 2-D, and 3-D. Detailed design-space exploration has been carried for a range of scaling parameters, with the results of various design trade-offs being presented and discussed. By taking advantage of abundant buildin reconfigurable logic and routing resources, we have been able to create a new scalable on-chip FPGA based router that exhibits high dimensionality and connectivity. The architecture proposed can be easily migrated across many FPGA families to provide flexible, robust and cost-effective NoC solutions suitable for the implementation of high-performance FPGA computing systems. © 2011 IEEE.

Swarm scheduling approaches for work-flow applications with security constraints in distributed data-intensive computing environments

The scheduling problem in distributed data-intensive computing environments has become an active research topic due to the tremendous growth in grid and cloud computing environments. As an innovative distributed intelligent paradigm, swarm intelligence provides a novel approach to solving these potentially intractable problems. In this paper, we formulate the scheduling problem for work-flow applications with security constraints in distributed data-intensive computing environments and present a novel security constraint model. Several meta-heuristic adaptations to the particle swarm optimization algorithm are introduced to deal with the formulation of efficient schedules. A variable neighborhood particle swarm optimization algorithm is compared with a multi-start particle swarm optimization and multi-start genetic algorithm. Experimental results illustrate that population based meta-heuristics approaches usually provide a good balance between global exploration and local exploitation and their feasibility and effectiveness for scheduling work-flow applications. © 2010 Elsevier Inc. All rights reserved.

True navigation in birds: from quantum physics to global migration

Birds are capable of true navigation, the ability to return to a known goal from a place they have never visited before. This is demonstrated most spectacularly during the vast migratory journeys made by these animals year after year, often between continents and occasionally global in nature. However, it remains one of the great unanswered questions in science, despite more than 50 years of research in this field. Nevertheless, the study of true navigation in birds has made significant advances in the previous 20 years, in part thanks to the integration of many disciplines outside its root in behavioural biology, to address questions of neurobiology, molecular aspects, and the physics of sensory systems and environmental cues involved in bird navigation, often involving quantum physics. However, true navigation remains a controversial field, with many conflicting and confusing results making interpretation difficult, particularly for those outside or new to the field. Unlike many general texts on migration, which avoid discussion of these issues, this review will present these conflicting findings and assess the state of the field of true navigation during bird migration.