6109

Exploring Many-Core Design Templates for FPGAs and ASICs

Ilia Lebedev, Christopher Fletcher, Shaoyi Cheng, James Martin, Austin Doupnik, Daniel Burke, Mingjie Lin, John Wawrzynek
CSAIL, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
International Journal of Reconfigurable Computing, Volume 2012, Article ID 439141, 15 pages, 2012

@article{lebedev2012exploring,

   title={Exploring Many-Core Design Templates for FPGAs and ASICs},

   author={Lebedev, I. and Fletcher, C. and Cheng, S. and Martin, J. and Doupnik, A. and Burke, D. and Lin, M. and Wawrzynek, J.},

   year={2012}

}

Download Download (PDF)   View View   Source Source   

724

views

We present a highly productive approach to hardware design based on a many-coremicroarchitectural template used to implement compute-bound applications expressed in a high-level data-parallel language such as OpenCL. The template is customized on a per-application basis via a range of high-level parameters such as the interconnect topology or processing element architecture. The key benefits of this approach are that it (i) allows programmers to express parallelism through an API defined in a high-level programming language, (ii) supports coarse-grainedmultithreading and fine-grained threading while permitting bit-level resource control, and (iii) reduces the effort required to repurpose the systemfor different algorithms or different applications.We compare template-driven design to both full-custom and programmable approaches by studying implementations of a compute-bound data-parallel Bayesian graph inference algorithm across several candidate platforms. Specifically, we examine a range of templatebased implementations on both FPGA and ASIC platforms and compare each against full custom designs. Throughout this study, we use a general-purpose graphics processing unit (GPGPU) implementation as a performance and area baseline. We show that our approach, similar in productivity to programmable approaches such as GPGPU applications, yields implementations with performance approaching that of full-custom designs on both FPGA and ASIC platforms.
No votes yet.
Please wait...

* * *

* * *

HGPU group © 2010-2017 hgpu.org

All rights belong to the respective authors

Contact us: