Design and Analysis of High Performance Heterogeneous Block-based Approximate Adders

TL;DR

This paper introduces Heterogeneous Block-based Approximate Adders (HBAA), a new energy-efficient design that offers improved power and area savings with configurable error trade-offs for approximate computing applications.

Contribution

The paper proposes a generic configurable model for HBAAs, enabling efficient design space exploration and demonstrating superior efficiency and error trade-offs over existing approximate adders.

Findings

HBAAs achieve around 15% area reduction.

HBAAs provide up to 17% energy savings.

The analytical model accurately evaluates error metrics.

Abstract

Approximate computing is an emerging paradigm to improve the power and performance efficiency of error-resilient applications. As adders are one of the key components in almost all processing systems, a significant amount of research has been carried out towards designing approximate adders that can offer better efficiency than conventional designs, however, at the cost of some accuracy loss. In this paper, we highlight a new class of energy-efficient approximate adders, namely Heterogeneous Block-based Approximate Adders (HBAA), and propose a generic configurable adder model that can be configured to represent a particular HBAA configuration. An HBAA, in general, is composed of heterogeneous sub-adder blocks of equal length, where each sub-adder can be an approximate sub-adder and have a different configuration. The sub-adders are mainly approximated through inexact logic and carry truncation. Compared to the existing design space, HBAAs provide additional design points that fall on the Pareto-front and offer a better quality-efficiency trade-off in certain scenarios. Furthermore, to enable efficient design space exploration based on user-defined constraints, we propose an analytical model to efficiently evaluate the Probability Mass Function (PMF) of approximation error and other error metrics, such as Mean Error Distance (MED), Normalized Mean Error Distance (NMED) and Error Rate (ER) of HBAAs. The results show that HBAA configurations can provide around 15% reduction in area and up to 17% reduction in energy compared to state-of-the-art approximate adders.