Architecture of Computing Systems -- ARCS 2016: 29th by Frank Hannig, João M.P. Cardoso, Thilo Pionteck, Dietmar

By Frank Hannig, João M.P. Cardoso, Thilo Pionteck, Dietmar Fey, Wolfgang Schröder-Preikschat, Jürgen Teich

This e-book constitutes the complaints of the twenty ninth overseas convention on structure of Computing platforms, ARCS 2016, held in Nuremberg, Germany, in April 2016.
The 29 complete papers offered during this quantity have been rigorously reviewed and chosen from 87 submissions. They have been prepared in topical sections named: configurable and in-memory accelerators; network-on-chip and safe computing architectures; cache architectures and protocols; mapping of functions on heterogeneous architectures and real-time initiatives on multiprocessors; all approximately time: timing, tracing, and function modeling; approximate and energy-efficient computing; allocation: from thoughts to FPGA modules; natural computing structures; and reliability elements in NoCs, caches, and GPUs.

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Additional resources for Architecture of Computing Systems -- ARCS 2016: 29th International Conference, Nuremberg, Germany, April 4-7, 2016, Proceedings

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NDA: near-DRAM acceleration architecture leveraging commodity DRAM devices and standard memory modules. In: 2015 IEEE 21st International Symposium on High Performance Computer Architecture (HPCA), pp. 283–295, February 2015 12. : Simulating DRAM controllers for future system architecture exploration. In: 2014 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS), pp. 201–210, March 2014 13. : Hybrid memory cube new DRAM architecture increases density and performance.

11. Single engine configurations were not shown in the Fig. 11 as they demonstrated 100 % utilization. As tasks (may) execute concurrently, Fig. 4- and 6-FUs) which is contrary to conventional processors which possess two ALUs. This fact is attributed to the physical compiler’s ability to extract additional ILP and DLP. Thus when only one task is to be executed at a given time, preference to is given to the larger engine which can provide the processor with higher performance and more flexibility for executing the task.

At the lowest level, a resident program runs on PIM performing the required tasks. A dynamic binary offloading mechanism has been designed to modify this code during runtime. PIM also features a set of configuration registers mapped in the physical address space and accessible by the host. PIM’s device driver has been adopted from Mali GPU’s driver [1] and is compatible with standard accelerators as well as parallel programming APIs such as OpenCL. This light-weight driver provides a lowoverhead and high-performance communication mechanism between the API and PIM.

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