Rapid prototyping and evaluation of programmable SIMD SDR processors in LISA

Ting Chen; Hengzhu Liu; Botao Zhang; Dongpei Liu

doi:10.1117/12.2014028

13 March 2013 Rapid prototyping and evaluation of programmable SIMD SDR processors in LISA

Ting Chen, Hengzhu Liu, Botao Zhang, Dongpei Liu

Proceedings Volume 8784, Fifth International Conference on Machine Vision (ICMV 2012): Algorithms, Pattern Recognition, and Basic Technologies; 87841D (2013) https://doi.org/10.1117/12.2014028
Event: Fifth International Conference on Machine Vision (ICMV 12), 2012, Wuhan, China

Abstract

With the development of international wireless communication standards, there is an increase in computational requirement for baseband signal processors. Time-to-market pressure makes it impossible to completely redesign new processors for the evolving standards. Due to its high flexibility and low power, software defined radio (SDR) digital signal processors have been proposed as promising technology to replace traditional ASIC and FPGA fashions. In addition, there are large numbers of parallel data processed in computation-intensive functions, which fosters the development of single instruction multiple data (SIMD) architecture in SDR platform. So a new way must be found to prototype the SDR processors efficiently. In this paper we present a bit-and-cycle accurate model of programmable SIMD SDR processors in a machine description language LISA. LISA is a language for instruction set architecture which can gain rapid model at architectural level. In order to evaluate the availability of our proposed processor, three common baseband functions, FFT, FIR digital filter and matrix multiplication have been mapped on the SDR platform. Analytical results showed that the SDR processor achieved the maximum of 47.1% performance boost relative to the opponent processor.

Citation Download Citation

Ting Chen, Hengzhu Liu, Botao Zhang, and Dongpei Liu "Rapid prototyping and evaluation of programmable SIMD SDR processors in LISA", Proc. SPIE 8784, Fifth International Conference on Machine Vision (ICMV 2012): Algorithms, Pattern Recognition, and Basic Technologies, 87841D (13 March 2013); https://doi.org/10.1117/12.2014028

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