Download Architecture of Computing Systems – ARCS 2012: 25th by Andreas Heinig, Vincent J. Mooney, Florian Schmoll, Peter PDF

By Andreas Heinig, Vincent J. Mooney, Florian Schmoll, Peter Marwedel, Krishna Palem (auth.), Andreas Herkersdorf, Kay Römer, Uwe Brinkschulte (eds.)

This ebook constitutes the refereed lawsuits of the twenty fifth overseas convention on structure of Computing platforms, ARCS 2012, held in Munich, Germany, in February/March 2012. The 20 revised complete papers awarded in 7 technical periods have been rigorously reviewed and chosen from sixty five submissions. The papers are prepared in topical sections on robustness and fault tolerance, power-aware processing, parallel processing, processor cores, optimization, and conversation and memory.

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In: Proceedings 23rd Symp. on Fault-Tolerant Computing Systems (FTCS-23), pp. 310–319 (1993) 6. : Experimental vehicle platform for pedestrian detection. California PATH research report. California PATH Program, Institute of Transportation Studies, University of California at Berkeley (2006) 7. : Barcelona openmp tasks suite: A set of benchmarks targeting the exploitation of task parallelism in openmp. In: Proceedings of the 2009 International Conference on Parallel Processing, ICPP 2009, pp. 124–131.

Brehm et al. Table 1. Resilience actuators on Different Abstraction Layers for Timing and Soft Errors in Turbo Code Decoding Do nothing HW Operation Point Algorithmic Parameters Algorithm Timing Errors — Vdd , f Number Iterations, Quantization log-MAP, max-log-MAP Soft errors — Vdd Number Iterations — Change the algorithmic parameters: Instead of changing the hardware operating point the number of decoding iterations can be increased (see Section 3). One additional iteration can counterbalance an increase in one order of magnitude in the flip event rate in the memories (see Fig.

Herkersdorf, K. R¨ omer, and U. ): ARCS 2012, LNCS 7179, pp. 37–49, 2012. c Springer-Verlag Berlin Heidelberg 2012 38 Y. Xu et al. design space without considering the performance metric will probably result in a local optimum and compromise the system performance. Thus, exploring the design space with both power consumption and performance design constraints considered is desirable. Existing methods usually fulfill the above requirements partially and have their own limitations. Spreadsheet based approaches [6] are commonly used to get rough power estimations at early design phases.

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