Abstract
Traditional microprocessors are today getting more and more inefficient for a growing range of applications that are mainly about processing data-stream. These applications have two character characteristics: one is that lots of intensive computation tasks need to be processed, another is that the running time of these tasks occupy more than 90% of total time. Coarse grained reconfigurable computation is very fitful for these tasks and can achieve very high performance. This paper presents implementation of the task of fast parallel complex FFT on CTaiJi, the 16bits Reconfigurable computation platform, which is targeting on streamed applications such as multi-media and DSP (digital signal processing). The proposed mapping comprises fast store-address transformation and configuring the function of PEA (processing element array) to fit for FFT. More-over, the performance is scalable according to FFT sizes. Since there is no functionality specifically tailored to FFT, the results demonstrate the capability of CTaiJi architecture to extract parallelism from streamed applications. Further ration- ales are given based on the concepts of scalar operand networks.
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Song, L., Jiang, Y. (2005). Fast Parallel FFT on CTaiJi: A Coarse-Grained Reconfigurable Computation Platform. In: Pan, Y., Chen, D., Guo, M., Cao, J., Dongarra, J. (eds) Parallel and Distributed Processing and Applications. ISPA 2005. Lecture Notes in Computer Science, vol 3758. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11576235_24
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DOI: https://doi.org/10.1007/11576235_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29769-7
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