Computing with Memory for Energy-Efficient Robust Systems

Bibliographic Details
Main Author:	Paul, Somnath
Language:	English
Published:	Case Western Reserve University School of Graduate Studies / OhioLINK 2011
Subjects:	Computer Engineering
Online Access:	http://rave.ohiolink.edu/etdc/view?acc_num=case1310143180

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spelling	ndltd-OhioLink-oai-etd.ohiolink.edu-case13101431802021-08-03T05:34:10Z Computing with Memory for Energy-Efficient Robust Systems Paul, Somnath Computer Engineering <p>Reconfigurable computing platforms that offer the flexibility to configure hardware resources according to application requirements, provide great opportunity to accelerate wide variety of applications. Over the past decade, Field Programmable Gate Arrays (FPGAs) have grown to be the most popular hardware reconfigurable computing platform. Modern FPGAs integrate an array of spatially distributed logic/memory blocks and programmable routing resources. Such a framework can provide several orders of magnitude more throughput compared to conventional microprocessor based designs. The power and performance of conventional FPGA platform is largely dominated by programmable interconnects, which have poor technological scalability. Moreover, the performance improvement for applications mapped to the FPGA platform is largely limited by the off-chip bandwidth. A reconfigurable framework which minimizes the contribution from the programmable interconnects and mitigates the bandwidth bottleneck by moving the computing engine close to the data is expected to significantly improve the energy efficiency of reconfigurable systems.</p> <p>In this work, we propose a novel hardware reconfigurable framework, referred to as memory based computing (MBC) framework. The main computing fabric for such a framework is a 2-D memory array which is used to store the functional behavior for the mapped application. Each computing element in the framework is temporal in nature and an array of these elements is used to map an application in a spatio-temporal fashion. Temporal execution inside each compute element reduces the requirement for programmable interconnects, thus improving the energy-efficiency over a fully spatial reconfigurable framework. In addition to storing the functional behavior, the memory arrays also store data, thus mitigating the off-chip bandwidth bottleneck. The framework is particularly appealing for system design with many emerging non-silicon nano-devices, which are amenable to dense, regular nonvolatile memory design. With the primary computing fabric being memory, the proposed MBC framework can be made robust to high device failure rates at nanoscale technologies. We have developed architecture and circuit level optimization techniques for the proposed framework along with efficient algorithms for automatic mapping of applications to this framework. Finally, we have investigated application of this framework as a reconfigurable computing resource in a processor for reliability improvement and hardware acceleration.</p> 2011-07-12 English text Case Western Reserve University School of Graduate Studies / OhioLINK http://rave.ohiolink.edu/etdc/view?acc_num=case1310143180 http://rave.ohiolink.edu/etdc/view?acc_num=case1310143180 unrestricted This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws.
collection	NDLTD
language	English
sources	NDLTD
topic	Computer Engineering
spellingShingle	Computer Engineering Paul, Somnath Computing with Memory for Energy-Efficient Robust Systems
author	Paul, Somnath
author_facet	Paul, Somnath
author_sort	Paul, Somnath
title	Computing with Memory for Energy-Efficient Robust Systems
title_short	Computing with Memory for Energy-Efficient Robust Systems
title_full	Computing with Memory for Energy-Efficient Robust Systems
title_fullStr	Computing with Memory for Energy-Efficient Robust Systems
title_full_unstemmed	Computing with Memory for Energy-Efficient Robust Systems
title_sort	computing with memory for energy-efficient robust systems
publisher	Case Western Reserve University School of Graduate Studies / OhioLINK
publishDate	2011
url	http://rave.ohiolink.edu/etdc/view?acc_num=case1310143180
work_keys_str_mv	AT paulsomnath computingwithmemoryforenergyefficientrobustsystems
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Computing with Memory for Energy-Efficient Robust Systems

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