Kalman filtering: theory and practice
Kalman filtering: theory and practice
Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Discrete-time signal processing (2nd ed.)
Discrete-time signal processing (2nd ed.)
A methodology and design environment for DSP ASIC fixed point refinement
DATE '99 Proceedings of the conference on Design, automation and test in Europe
Bidwidth analysis with application to silicon compilation
PLDI '00 Proceedings of the ACM SIGPLAN 2000 conference on Programming language design and implementation
Precision and error analysis of MATLAB applications during automated hardware synthesis for FPGAs
Proceedings of the conference on Design, automation and test in Europe
Accuracy Sensitive Word--Length Selection for Algorithm Optimization
ICCD '98 Proceedings of the International Conference on Computer Design
Automated fixed-point data-type optimization tool for signal processing and communication systems
Proceedings of the 41st annual Design Automation Conference
Design and DSP implementation of fixed-point systems
EURASIP Journal on Applied Signal Processing
Optimum wordlength search using sensitivity information
EURASIP Journal on Applied Signal Processing
Wordlength optimization for linear digital signal processing
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Journal of Signal Processing Systems
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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.