| Streaming FFT on REDEFINE-v2: an application-architecture design space exploration |
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International Conference on Compilers, Architecture and Synthesis for Embedded Systems
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Proceedings of the 2009 international conference on Compilers, architecture, and synthesis for embedded systems
table of contents
Grenoble, France
SESSION: Architectural optimizations
table of contents
Pages 127-136
Year of Publication: 2009
ISBN:978-1-60558-626-7
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Authors
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Alexander Fell
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Indian Institute of Science, Bangalore, India
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Mythri Alle
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Indian Institute of Science, Bangalore, India
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Keshavan Varadarajan
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Indian Institute of Science, Bangalore, India
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Prasenjit Biswas
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Indian Institute of Science, Bangalore, India
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Saptarsi Das
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Indian Institute of Science, Bangalore, India
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Jugantor Chetia
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Indian Institute of Science, Bangalore, India
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S. K. Nandy
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Indian Institute of Science, Bangalore, India
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Ranjani Narayan
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Morphing Machines, Bangalore, India
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ABSTRACT
In this paper we explore an implementation of a high-throughput, streaming application on REDEFINE-v2, which is an enhancement of REDEFINE. REDEFINE is a polymorphic ASIC combining the flexibility of a programmable solution with the execution speed of an ASIC. In REDEFINE Compute Elements are arranged in an 8x8 grid connected via a Network on Chip (NoC) called RECONNECT, to realize the various macrofunctional blocks of an equivalent ASIC. For a 1024-FFT we carry out an application-architecture design space exploration by examining the various characterizations of Compute Elements in terms of the size of the instruction store. We further study the impact by using application specific, vectorized FUs. By setting up different partitions of the FFT algorithm for persistent execution on REDEFINE-v2, we derive the benefits of setting up pipelined execution for higher performance. The impact of the REDEFINE-v2 micro-architecture for any arbitrary N-point FFT (N > 4096) FFT is also analyzed. We report the various algorithm-architecture tradeoffs in terms of area and execution speed with that of an ASIC implementation. In addition we compare the performance gain with respect to a GPP.
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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