A Flexible DA-Based Architecture for Computation of Inner Product of Variable Vectors

Abstract

The computation of inner products of any given pair of vectors is an indispensable requirement in several applications including artificial intelligence (AI), machine learning (ML), signal processing, image processing, communication, and many others. The throughput requirement of inner product computation varies widely for different applications. Moreover, the throughput of computation must match the requirements of the applications. It is therefore important to design flexible hardware for inner product computation that produces the desired throughput. Distributed arithmetic (DA) is a well-known approach for efficient inner product computation. This article presents an efficient DA-based architecture for computing the inner product of variable vectors, which could be tailored according to the throughput requirement of any given application and reused for different inner product lengths. The proposed designs could also be deployed to achieve a trade-off between throughput and area/energy consumption. In this article, we have used modified Booth encoding (MBE) to reduce the number of partial products and proposed a novel carry-save accumulator (CSA) for shortening the critical path delay. The proposed designs are synthesized by Cadence Genus using GPDK 90-nm technology library and place-and-route using Cadence Innovus for different inner product lengths and word lengths. As found from the postlayout synthesis results, the proposed designs offer savings of nearly 30% and 29% EPC and ADP over the bit-serial DA-based design on average for word lengths 8 and 16 and inner product lengths 8, 16, and 32, respectively.