Reducing switching activity in FIR filters by reordering the coefficients through the use of improved heuristic algorithm

Low power FIR filter design using modified multi-objective artificial bee colony algorithm

“…Similarly for power minimization, the corresponding objective function has been formulated by considering Hamming distance between filter coefficients. Power consumption in FIR filters can be minimized by reducing hardware complexity through filter implementation architecture (Arslan et al, 1996;Azarmehr and Ahmadi, 2012;Su et al, 1994;Mehendale et al, 1998;Hong et al, 2002;Xie et al, 2010) or by reducing switching activities between filter coefficients (Kavitha and Sasikumar, 2014;Najm, 1993;Nemani and Najm, 1996;Rahmeier et al, 2013;Shao et al, 2006) in their binary form, while processing through data buses of FPGA. In existing literature, Hamming distance (HD) between successive coefficients (Aktan et al, 2008;Gustafsson and Wanhammar, 2002;Mehendale et al, 1995;Merakos et al, 1997;Sankarayya et al, 1997) has been considered as a measure of switching activity.…”

“…In Aktan et al (2004), an iterative mean field annealing algorithm has been used for reducing HD between successive filter coefficients. A heuristic Anedma algorithm and its improved version has been used for reducing switching activity in FIR filters (Rahmeier et al, 2013). The reordering of filter coefficients has been formulated as traveling salesman problem for reducing switching activity (Merakos et al, 1997).…”

Low power 2D finite impulse response filter design using modified artificial bee colony algorithm with experimental validation using field‐programmable gate array

“…In [27], an iterative mean field annealing algorithm has been used for reducing HD between successive filter coefficients. A heuristic Anedema algorithm and its improved version have been used for reducing switching activity in FIR filters [28]. HD reduction has been performed using five different techniques, i.e.…”