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VESPA: portable, scalable, and flexible FPGA-based vector processors
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International Conference on Compilers, Architecture and Synthesis for Embedded Systems archive
Proceedings of the 2008 international conference on Compilers, architectures and synthesis for embedded systems table of contents
Atlanta, GA, USA
SESSION: Reconfigurable computing table of contents
Pages 61-70  
Year of Publication: 2008
ISBN:978-1-60558-469-0
Authors
Peter Yiannacouras  University of Toronto, Toronto, ON, Canada
J. Gregory Steffan  University of Toronto, Toronto, ON, Canada
Jonathan Rose  University of Toronto, Toronto, ON, Canada
Sponsors
SIGDA: ACM Special Interest Group on Design Automation
ACM: Association for Computing Machinery
SIGBED: ACM Special Interest Group on Embedded Systems
SIGMICRO: ACM Special Interest Group on Microarchitectural Research and Processing
Publisher
ACM  New York, NY, USA
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ABSTRACT

While soft processors are increasingly common in FPGA-based embedded systems, it remains a challenge to scale their performance. We propose extending soft processor instruction sets to include support for vector processing. The resulting system of vectorized software and soft vector processor hardware is (i) portable to any FPGA architecture and vector processor configuration, (ii) scalable to larger yet higher-performance designs, and (iii) flexible, allowing the underlying vector processor to be customized to match the needs of each application. Using our robust and verified parameterized vector processor design and industry-standard EEMBC benchmarks, we evaluate the performance and area trade-offs for different soft vector processor configurations using an FPGA development platform with DDR SDRAM. We find that on average we can scale performance from 1.8x up to 6.3x for a vector processor design that saturates the capacity of our platform's Stratix 1S80 FPGA. We also automatically generate application-specific vector processors with reduced datapath width and instruction set support which combined reduce the area by up to 70% (61% on average) without affecting performance.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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M. Labrecque, P. Yiannacouras, and J. G. Steffan. Scaling Soft Memory Systems. In IEEE Symposium on Field-Programmable Custom Computing Machines (FCCM'08)., Palo Alto, CA, April 2008.
 
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Collaborative Colleagues:
Peter Yiannacouras: colleagues
J. Gregory Steffan: colleagues
Jonathan Rose: colleagues