Multi-objective Approach for IIR Filter Design and Bit-Width Optimization

• Published in: Circuits, systems, and signal processing Vol. 42; no. 8; pp. 4836 - 4867
• Main Authors: Zelmat, Mohammed, Lamini, El-Sedik, Tagzout, Samir, Belbachir, Hacène, Belouchrani, Adel
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.08.2023; Springer Nature B.V
• Subjects: Algorithms; Circuit design; Circuits and Systems; Design optimization; Digital filters; Electrical Engineering; Electronics and Microelectronics; Engineering; Filter design (mathematics); Genetic algorithms; IIR filters; Impulse response; Instrumentation; Mathematical programming; Multiple objective analysis; Signal,Image and Speech Processing; Sorting algorithms; Bit-width optimization; Non-dominated sorting genetic algorithm II; System identification; Filter design; Precision analysis; IC design
• ISSN: 0278-081X; 1531-5878
• DOI: 10.1007/s00034-023-02334-1

Digital filter design optimization counts as one of the most challenging problems in circuit development. Furthermore, the bit-width allocation has a crucial impact on the efficiency and accuracy of fixed-point digital filters. The conventional implementation procedure involves studying the bit-width allocation problem after determining the filter coefficients. This sequential procedure leads frequently to an excessive bit-width allocation, hence an extra implementation cost. The main contribution of this paper is to propose a new implementation procedure based on the simultaneous handling of both design issues and the bit-width optimization for the infinite impulse response filter. In this study, the problem is formalized by a multi-objective programming technique using the non-dominated sorting genetic algorithm II. The joint consideration of the two problems offers a better trade-off between design and bit-width allocation. The output of our approach is compared to the results of existing algorithms. Simulation results show that the multi-objective method significantly reduces the implementation cost while ensuring filter stability and an acceptable mean square error (MSE) compared to the existing algorithms.