Published: Oct 1, 2012
Converted to Gold OA:
DOI: 10.4018/ijaras.20121001.pre
Volume 3
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DOI: 10.4018/jaras.2012100101
Volume 3
Snaider Carrillo, Neil McDonnell, Jim Harkin, Liam McDaid
Recent focus has been placed on exploring the possibility to switch from parallel to serial data links between NoC routers in order to improve signal integrity in the communication channel. However...
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Recent focus has been placed on exploring the possibility to switch from parallel to serial data links between NoC routers in order to improve signal integrity in the communication channel. However, moving streams of data between the parallel path of the internal router and external serial-channel links between them consumes additional power. One challenge is encoding the data and minimise the switching activity of data in the serial links in order to reduce the additional power dissipation; while under real-time and minimal hardware constraints. Consequently, proposed is a novel low area/power decision circuit for NoC channel encoding which identifies in real-time packets for encoding and extends the existing SILENT encoders/decoders to further minimise power consumption and demonstrates the power performance savings of the decision circuit and modified (en)decoders using example test traffic with the EMBRACE NoC router, a mixed signal spiking neural network (SNNs) embedded platform.
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MLA
McDonnell, Neil, et al. "Investigating Power Reduction for NoC-Based Spiking Neural Network Platforms using Channel Encoding." IJARAS vol.3, no.4 2012: pp.1-16. http://doi.org/10.4018/jaras.2012100101
APA
McDonnell, N., Carrillo, S., Harkin, J., & McDaid, L. (2012). Investigating Power Reduction for NoC-Based Spiking Neural Network Platforms using Channel Encoding. International Journal of Adaptive, Resilient and Autonomic Systems (IJARAS), 3(4), 1-16. http://doi.org/10.4018/jaras.2012100101
Chicago
McDonnell, Neil, et al. "Investigating Power Reduction for NoC-Based Spiking Neural Network Platforms using Channel Encoding," International Journal of Adaptive, Resilient and Autonomic Systems (IJARAS) 3, no.4: 1-16. http://doi.org/10.4018/jaras.2012100101
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Published: Oct 1, 2012
Converted to Gold OA:
DOI: 10.4018/jaras.2012100102
Volume 3
Kyrre Glette, Paul Kaufmann, Marco Platzner, Jim Torresen
The evolvable hardware (EHW) paradigm facilitates the construction of autonomous systems that can adapt to environmental changes and degradation of the computational resources. Extending the EHW...
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The evolvable hardware (EHW) paradigm facilitates the construction of autonomous systems that can adapt to environmental changes and degradation of the computational resources. Extending the EHW principle to architectural adaptation, the authors study the capability of evolvable hardware classifiers to adapt to intentional run-time fluctuations in the available resources, i.e., chip area, in this work. To that end, the authors leverage the Functional Unit Row (FUR) architecture, a coarse-grained reconfigurable classifier, and apply it to two medical benchmarks, the Pima and Thyroid data sets from the UCI Machine Learning Repository. While quick recovery from architectural changes was already demonstrated for the FUR architecture, the authors also introduce two reconfiguration schemes helping to reduce the magnitude of degradation after architectural reconfiguration.
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MLA
Kaufmann, Paul, et al. "Compensating Resource Fluctuations by Means of Evolvable Hardware: The Run-Time Reconfigurable Functional Unit Row Classifier Architecture." IJARAS vol.3, no.4 2012: pp.17-31. http://doi.org/10.4018/jaras.2012100102
APA
Kaufmann, P., Glette, K., Platzner, M., & Torresen, J. (2012). Compensating Resource Fluctuations by Means of Evolvable Hardware: The Run-Time Reconfigurable Functional Unit Row Classifier Architecture. International Journal of Adaptive, Resilient and Autonomic Systems (IJARAS), 3(4), 17-31. http://doi.org/10.4018/jaras.2012100102
Chicago
Kaufmann, Paul, et al. "Compensating Resource Fluctuations by Means of Evolvable Hardware: The Run-Time Reconfigurable Functional Unit Row Classifier Architecture," International Journal of Adaptive, Resilient and Autonomic Systems (IJARAS) 3, no.4: 17-31. http://doi.org/10.4018/jaras.2012100102
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Published: Oct 1, 2012
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DOI: 10.4018/jaras.2012100103
Volume 3
Yang Liu, James Alfred Walker, Gianluca Tempesti, Jon Timmis, Andy M. Tyrrell
Transport triggered architectures are used for implementing bio-inspired systems due to their simplicity, modularity and fault-tolerance. However, producing efficient, optimised machine code for...
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Transport triggered architectures are used for implementing bio-inspired systems due to their simplicity, modularity and fault-tolerance. However, producing efficient, optimised machine code for such architectures is extremely difficult, since computational complexity has moved from the hardware-level to the software-level. Presented is the application of Cartesian Genetic Programming (CGP) to the evolution of machine code for a simple implementation of transport triggered architecture. The effectiveness of the algorithm is demonstrated by evolving machine code for a 4-bit multiplier with three different levels of parallelism. The results show that 100% successful solutions were found by CGP and by further optimising the size of the solutions, it’s possible to find efficient implementations of the 4-bit multiplier. Further analysis of the solutions showed that use of loops within the CGP function set could be beneficial and was demonstrated by repeating the earlier 4-bit multiplier experiment with the addition of a loop function.
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MLA
Walker, James Alfred, et al. "Automatic Machine Code Generation for a Transport Triggered Architecture using Cartesian Genetic Programming." IJARAS vol.3, no.4 2012: pp.32-50. http://doi.org/10.4018/jaras.2012100103
APA
Walker, J. A., Liu, Y., Tempesti, G., Timmis, J., & Tyrrell, A. M. (2012). Automatic Machine Code Generation for a Transport Triggered Architecture using Cartesian Genetic Programming. International Journal of Adaptive, Resilient and Autonomic Systems (IJARAS), 3(4), 32-50. http://doi.org/10.4018/jaras.2012100103
Chicago
Walker, James Alfred, et al. "Automatic Machine Code Generation for a Transport Triggered Architecture using Cartesian Genetic Programming," International Journal of Adaptive, Resilient and Autonomic Systems (IJARAS) 3, no.4: 32-50. http://doi.org/10.4018/jaras.2012100103
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Published: Oct 1, 2012
Converted to Gold OA:
DOI: 10.4018/jaras.2012100104
Volume 3
Spela Ivekovic, Luca Mussi, Youssef S.G. Nashed, Stefano Cagnoni
The authors formulate the body pose estimation as a multi-dimensional nonlinear optimization problem, suitable to be approximately solved by a meta-heuristic, specifically, the particle swarm...
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The authors formulate the body pose estimation as a multi-dimensional nonlinear optimization problem, suitable to be approximately solved by a meta-heuristic, specifically, the particle swarm optimization (PSO). Starting from multi-view video sequences acquired in a studio environment, a full skeletal configuration of the human body is retrieved. They use a generic subdivision-surface body model in 3-D to generate solutions for the optimization problem. PSO then looks for the best match between the silhouettes generated by the projection of the model in a candidate pose and the silhouettes extracted from the original video sequence. The optimization method, in this case PSO, is run in parallel on the Graphics Processing Unit (GPU) and is implemented in Cuda-C™ on the nVidia CUDA™ architecture. The authors compare the results obtained by different configurations of the camera setup, fitness function, and PSO neighborhood topologies.
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MLA
Mussi, Luca, et al. "Multi-View Human Body Pose Estimation with CUDA-PSO." IJARAS vol.3, no.4 2012: pp.51-65. http://doi.org/10.4018/jaras.2012100104
APA
Mussi, L., Ivekovic, S., Nashed, Y. S., & Cagnoni, S. (2012). Multi-View Human Body Pose Estimation with CUDA-PSO. International Journal of Adaptive, Resilient and Autonomic Systems (IJARAS), 3(4), 51-65. http://doi.org/10.4018/jaras.2012100104
Chicago
Mussi, Luca, et al. "Multi-View Human Body Pose Estimation with CUDA-PSO," International Journal of Adaptive, Resilient and Autonomic Systems (IJARAS) 3, no.4: 51-65. http://doi.org/10.4018/jaras.2012100104
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