Shaoyi Cheng

EECS Department, University of California, Berkeley

Technical Report No. UCB/EECS-2013-40

May 1, 2013

http://www2.eecs.berkeley.edu/Pubs/TechRpts/2013/EECS-2013-40.pdf

As memory accesses increasingly limit the overall performance of reconfigurable accelerators, it is important for high-level synthesis (HLS) flows to adopt a systematic way to discover and exploit memory-level parallelism. This work develops 1) a framework where parallelism between memory accesses can be revealed from runtime profiles of applications and provided to a high level synthesis flow, and 2) a novel multiaccelerator/multi-cache architecture to support parallel memory accesses, taking advantage of the high aggregated memory bandwidth found in modern FPGA devices. Our experimental results have shown that for 10 accelerators generated from 9 benchmark applications, circuits using our proposed memory structure achieve on average 51% improved performance over accelerators using a traditional memory interface. We believe that our study represents a solid advance towards achieving memory-parallel embedded computing on hybrid CPU+FPGA platforms.

Advisors: John Wawrzynek

---

BibTeX citation:

@mastersthesis{Cheng:EECS-2013-40,
    Author= {Cheng, Shaoyi},
    Title= {Exploiting Memory-level Parallelism in Reconfigurable Accelerators},
    School= {EECS Department, University of California, Berkeley},
    Year= {2013},
    Month= {May},
    Url= {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2013/EECS-2013-40.html},
    Number= {UCB/EECS-2013-40},
    Abstract= {As memory accesses increasingly limit the overall performance of reconfigurable accelerators, it is important for high-level synthesis (HLS) flows to adopt a systematic way to discover and exploit memory-level parallelism. This work develops 1) a framework where parallelism between memory
accesses can be revealed from runtime profiles of applications and provided to a high level synthesis flow, and 2) a novel multiaccelerator/multi-cache architecture to support parallel memory accesses, taking advantage of the high aggregated memory bandwidth found in modern FPGA devices. Our
experimental results have shown that for 10 accelerators generated from 9 benchmark applications, circuits using our proposed memory structure achieve on average 51% improved performance over accelerators using a traditional memory interface. We believe that our study represents a solid advance towards achieving memory-parallel embedded computing on hybrid CPU+FPGA platforms.},
}

EndNote citation:

%0 Thesis
%A Cheng, Shaoyi 
%T Exploiting Memory-level Parallelism in Reconfigurable Accelerators
%I EECS Department, University of California, Berkeley
%D 2013
%8 May 1
%@ UCB/EECS-2013-40
%U http://www2.eecs.berkeley.edu/Pubs/TechRpts/2013/EECS-2013-40.html
%F Cheng:EECS-2013-40