AxPPA Approximate Parallel Prefix Adders

Addition units are widely used in many computational kernels of several error-tolerant applications such as machine learning and signal, image, and video processing. Besides their use as stand-alone, additions are essential building blocks for other math operations such as subtraction, comparison, multiplication, squaring, and division. The parallel prefix adders (PPAs) is among the fastest adders. It represents a parallel prefix graph consisting of the carry operator nodes, called prefix operators (POs). The PPAs, in particular, are among the fastest adders because they optimize the parallelization of the carry generation (G) and propagation (P). In this work, we introduce approximate PPAs (AxPPAs) by exploiting approximations in the POs. To evaluate our proposal for approximate POs (AxPOs), we generate the following AxPPAs, consisting of a set of four PPAs: approximate Brent–Kung (AxPPA-BK), approximate Kogge–Stone (AxPPA-KS), Ladner-Fischer (AxPPA-LF), and Sklansky (AxPPA-SK). We compare four AxPPA architectures with energy-efficient approximate adders (AxAs) [i.e., Copy, error-tolerant adder I (ETAI), lower-part OR adder (LOA), and Truncation (trunc)]. We tested them generically in stand-alone cases and embedded them in two important signal processing application kernels: a sum of squared differences (SSDs) video accelerator and a finite impulse response (FIR) filter kernel. The AxPPA-LF provides a new Pareto front in both energy-quality and area-quality results compared to state-of-the-art energy-efficient AxAs.