A novel DA-based architecture for efficient computation of inner-product of variable vectors

Distributed arithmetic (DA) has been widely used for area-time efficient implementation of inner-products, where one of the vectors is fixed and known a priori. Computation of inner-product of a pair of variable vectors, however, is required very often for matrix-multiplication of different forms, and implementation of digital filters of unknown coefficients and variable lengths. But the possibility of using DA for the computation of inner-product of variable vectors is yet to be explored. In this paper, we analyse the design issues relating to DA-based implementation of inner-product of variable vectors, and derive a novel area-time efficient flexible solution for the bit-parallel DA-based implementation of inner-product of variable vectors and variable inner-product lengths. It is found that the proposed structures are nearly 34% faster than the conventional multiplier-based implementation in average for different inner-product lengths (N = 8, 16, 32 and 64) and for input word-lengths, L = 8 and L = 16. Moreover, proposed designs offer saving of nearly 22% and 36% area-delay product (ADP) and saving of nearly 16% and 24% power delay product (PDP) over the multiplier-based designs for L = 8 and L = 16, respectively, in average, for various inner-product lengths.