Algorithms for low power FIR filter realization using differential coefficients

Algorithms for low power and high speed FIR filter realization using differential coefficients

“…We present a new algorithm-level technique for low power, high speed realization of FIR filters, which we term the differences method (DCM) [8]. It uses various orders of differences between the coefficients along with stored precomputed results rather than the coefficients to compute the convolution in (1).…”

“…We define the second-order difference ( Fig. 2) between two consecutive first-order differences using the relation to (7) Except and , all other coefficients can be written as to (8) which follows from substituting in (3) the expression for from (7). Multiplying both sides of (8) by we obtain the product terms for computing as to (9) Using terminology analogous to that used for the first-order differences algorithm, we call the last two terms on the righthand side (RHS) of (9) as partial products and all three terms on the RHS as intermediate results.…”

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

“…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 other words, power consumption reduction in filter execution has been achieved by reducing the HD between the coefficients of the designed filter.…”

“…The HD reduction algorithm presented in Mehendale et al (1995) uses coefficient scaling and perturbation with iterative steepest descent strategy. In Sankarayya et al (1997) an iterative coefficient optimization algorithm has been proposed for HD reduction. In Aktan et al (2004), an iterative mean field annealing algorithm has been used for reducing HD between successive filter coefficients.…”

Survey of low power techniques for ROMs

“…For digital filters and other applications the ROM is accessed sequentially. If the values between adjacent data do not change significantly between one address and the next, the ROM core can store the difference between the data instead of the whole value [4]. The main disadvantage is that an adder is required to calculate the original value.…”