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TEA: Timing and Energy Aware compression architecture for Efficient Configuration in CGRAs
KTH, School of Information and Communication Technology (ICT), Electronics and Embedded Systems. University of Turku, Finland.
KTH, School of Information and Communication Technology (ICT), Electronics and Embedded Systems. University of Turku, Finland.
KTH, School of Information and Communication Technology (ICT), Electronics and Embedded Systems.ORCID iD: 0000-0003-0565-9376
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2015 (English)In: Microprocessors and microsystems, ISSN 0141-9331, E-ISSN 1872-9436Article in journal (Refereed) Published
Abstract [en]

Coarse Grained Reconfigurable Architectures (CGRAs) are emerging as enabling platforms to meet the high performance demanded by modern applications (e.g. 4G, CDMA, etc.). Recently proposed CGRAs offer time-multiplexing and dynamic applications parallelism to enhance device utilization and reduce energy consumption at the cost of additional memory (up to 50% area of the overall platform). To reduce the memory overheads, novel CGRAs employ either statistical compression, intermediate compact representation, or multicasting. Each compaction technique has different properties (i.e. compression ratio, decompression time and decompression energy) and is best suited for a particular class of applications. However, existing research only deals with these methods separately. Moreover, they only analyze the compaction ratio and do not evaluate the associated energy overheads. To tackle these issues, we propose a polymorphic compression architecture that interleaves these techniques in a unique platform. The proposed architecture allows each application to take advantage of a separate compression/decompression hierarchy (consisting of various types and implementations of hardware/software decoders) tailored to its needs. Simulation results, using different applications (FFT, Matrix multiplication, and WLAN), reveal that the choice of compression hierarchy has a significant impact on compression ratio (up to 52%), decompression energy (up to 4 orders of magnitude), and configuration time (from 33. n to 1.5. s) for the tested applications. Synthesis results reveal that introducing adaptivity incurs negligible additional overheads (1%) compared to the overall platform area.

Place, publisher, year, edition, pages
Elsevier, 2015.
Keyword [en]
Adaptive systems, CGRA configuration, Coarse grained reconfigurable architectures, Compression, Compression/decompression hierarchy, Reconfigurable architectures, Compression ratio (machinery), Cost reduction, Energy utilization, Memory architecture, Multicasting, Multiplexing, Power management, Coarse grained reconfigurable architecture, Coarse grained reconfigurable architecture (CGRAs), Compact representation, MAtrix multiplication, Proposed architectures, Reduce energy consumption, Statistical compression, Compaction
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:kth:diva-177258DOI: 10.1016/j.micpro.2015.05.002ISI: 000366879500037Scopus ID: 2-s2.0-84930370884OAI: oai:DiVA.org:kth-177258DiVA: diva2:872610
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VINNOVA
Note

QC 20151119. QC 20160121

Available from: 2015-11-19 Created: 2015-11-17 Last updated: 2017-12-01Bibliographically approved

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Hemani, Ahmed

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Jafri, Syed M. A. H.Daneshtalab, MasoudHemani, Ahmed
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