An Accuracy-Adjustment Fixed-Width Booth Multiplier Based on Multilevel Conditional Probability

This brief proposes an accuracy-adjustment fixed-width Booth multiplier that compensates the truncation error using a multilevel conditional probability (MLCP) estimator and derives a closed form for various bit widths L and column information w. Compared with the exhaustive simulations strategy, the proposed MLCP estimator substantially reduces simulation time and easily adjusts accuracy based on mathematical derivations. Unlike previous conditional-probability methods, the proposed MLCP uses entire nonzero code, namely MLCP, to estimate the truncation error and achieve higher accuracy levels. Furthermore, the simple and small MLCP compensated circuit is proposed in this brief. The results of this brief show that the proposed MLCP Booth multipliers achieve low-cost high-accuracy performance.