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A VLSI array processing oriented fast fourier transform algorithm and hardware implementation
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Source Great Lakes Symposium on VLSI archive
Proceedings of the 15th ACM Great Lakes symposium on VLSI table of contents
Chicago, Illinois, USA
SESSION: High-level low power design II table of contents
Pages: 291 - 295  
Year of Publication: 2005
ISBN:1-59593-057-4
Authors
Zhenyu Liu  Waseda University, Kitakyushu City, Japan
Yang Song  Waseda University, Kitakyushu City, Japan
Takeshi Ikenaga  Waseda University, Kitakyushu City, Japan
Satoshi Goto  Waseda University, Kitakyushu City, Japan
Sponsors
SIGDA: ACM Special Interest Group on Design Automation
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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ABSTRACT

Many parallel Fast Fourier Transform (FFT) algorithms adopt multiple stages architecture to increase performance. However, data permutation between stages consumes volume memory and processing time. An FFT array processing mapping algorithm is proposed in this paper to overcome this demerit. In this algorithm, arbitrary 2k butterfly units (BUs) could be scheduled to work in parallel on n=22 data (k=0, 1,..., s-1). Because no inter stage data transfer is required, memory consumption is reduced to 1/3 of the original algorithm. Moreover, with the increasing of BUs, not only does throughput increase linearly, system latency also decreases linearly. This array processing orientated architecture provides flexible tradeoff between hardware cost and system performance. An 18-bit word-length 1024-point FFT architecture with 4 BUs is given to demonstrate this mapping algorithm. The design is implemented with TSMC 0.18μm CMOS technology. The core area is 2.99x1.12mm 2 and clock frequency is 326MHz in typical condition (1.8V, 25°C). This processor could complete 1024 FFT calculation in 7.839μs.


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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Collaborative Colleagues:
Zhenyu Liu: colleagues
Yang Song: colleagues
Takeshi Ikenaga: colleagues
Satoshi Goto: colleagues