Symbolic noise analysis approach to computational hardware optimization
Proceedings of the 45th annual Design Automation Conference
Fast trade-off evaluation for digital signal processing systems during wordlength optimization
Proceedings of the 2009 International Conference on Computer-Aided Design
Bit-width allocation for hardware accelerators for scientific computing using SAT-modulo theory
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Finite precision bit-width allocation using SAT-modulo theory
Proceedings of the Conference on Design, Automation and Test in Europe
SQNR estimation of fixed-point DSP algorithms
EURASIP Journal on Advances in Signal Processing
EURASIP Journal on Advances in Signal Processing - Special issue on quantization of VLSI digital signal processing systems
Synthesis of minimal-error control software
Proceedings of the tenth ACM international conference on Embedded software
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Traditional approaches for fixed-point characterization are based on long simulations or additive noise models that do not show the nonlinear behavior of the quantization operations in reasonable time. In this paper, a novel interval-based approach that provides both tighter and faster results than the existing approaches is presented. It is based on a nonlinear adaptation of the quantization operations of affine arithmetic (AA). The results obtained with this method are compared to other published interval-based approaches, and the problem of interval oversizing is discussed in detail. Simulations show that: 1) the propagation techniques are not well suited to characterize the quantized linear systems with feedback loops; 2) the AA provides oversized bounds in this type of systems; and 3) the proposed adaptation does not provide guaranteed bounds as in the traditional interval-based computations, but it provides tighter estimates of the evolution in time of the ranges of the fixed-point signals and better runtime than the existing interval-based characterization techniques.