Improving approximate-TMR using multi-objective optimization genetic algorithm

Abstract

Approximate Triple Modular Redundancy (ATMR), which is the implementation of TMR with approximate versions of the target circuit, has emerged in recent years as an alternative to partial replication. This work presents a novel approach for implementing approximate TMR that combines an approximate gate library (ApxLib) with a Multi-Objective Optimization Genetic Algorithm (MOOGA). The algorithm initially performs a blind search, over the huge solution space, optimizing error coverage and area overhead altogether over the next interactions. Experiments compare our approach with a state of the art technique showing an improvement of trade-offs for different benchmark circuits.