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Numerical linear algebra algorithms use the inherent elegance of matrix formulations and are usually implemented using C/C++ floating point representation. The system implementation is faced with practical constraints because these algorithms usually need to run in real time on fixed point digital signal processors (DSPs) to reduce total hardware costs. Converting the simulation model to fixed point arithmetic and then porting it to a target DSP device is a difficult and time-consuming process. In this paper, we analyze the conversion process. We transformed selected linear algebra algorithms from floating point to fixed point arithmetic, and compared real-time requirements and performance between the fixed point DSP and floating point DSP algorithm implementations. We also introduce an advanced code optimization and an implementation by DSP-specific, fixed point C code generation. By using the techniques described in the paper, speed can be increased by a factor of up to 10 compared to floating point emulation on fixed point hardware.Gene Frantz is responsible for finding new opportunities and creating new businesses utilizing TI's digital signal processing technology. Frantz has been with Texas Instruments for over thirty years, most of it in digital signal processing. He is a recognized leader in DSP technology both within TI and throughout the industry. Frantz is a Fellow of the Institution of Electric and Electronics Engineers. He holds 40 patents in the area of memories, speech, consumer products and DSP. He has written more than 50 papers and articles and continually presents at universities and conferences worldwide. Frantz is also among industry experts widely quoted in the media due to his tremendous knowledge and visionary view of DSP solutions.

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