Cell Mapping for Nanohybrid Circuit Architecture Using Genetic Algorithm

Abstract

Nanoelectronics constructed by nanoscale devices seems promising for the advanced development of integrated circuits (ICs). However, the lack of computer aided design (CAD) tools seriously hinders its development and applications. To investigate the cell mapping task in CAD flow, we present a genetic algorithm (GA) based method for Cmos/nanowire/MOLecular hybrid (CMOL), which is a nanohybrid circuit architecture. By designing several crossover operators and analyzing their performance, an efficient crossover operator is proposed. Combining a mutation operator, a GA based algorithm is presented and tested on the International Symposium on Circuits and Systems (ISCAS) benchmarks. The results show that the proposed method not only can obtain better area utilization and smaller delay, but also can handle larger benchmarks with CPU time improvement compared with the published methods.

Fine-Grained Defect Diagnosis for CMOL FPGA Circuits

“…Among the various researches related to the nanoscale circuits, nano-programmable logic arrays (nano-PLAs) [8][9][10] and CMOS-nanowire-MOLecular (CMOL) hybrid circuits [11][12][13] are attracting considerable attention. Consequently, various defecttolerant methodologies for nano-PLAs [9,[14][15] and CMOL circuits [16][17][18] have been proposed. Accordingly, defect maps must be generated based on effective and accurate test and diagnosis for successful defect-tolerant logical mapping of nanoscale circuits.…”

Balancing exploration and exploitation by using sequential execution cooperation between artificial bee colony and migrating birds optimization algorithms

“…The genetic algorithm (GA) [4], the simulated annealing (SA) algorithm [5], the tabu search (TS) algorithm [6], the ant colony optimization (ACO) algorithm [7], the particle swarm optimization (PSO) algorithm [8], the differential evolution (DE) algorithm [9], the harmony search (HS) algorithm [10], the monkey search (MS) algorithm [11], the ABC algorithm [12], the firefly algorithm (FA) [13], the intelligent water drops (IWD) algorithm [14], the cuckoo search (CS) algorithm [15,16], the bat algorithm (BA) [17,18] and the MBO algorithm [19]. In parallel with these studies many researchers have developed new methodologies based on the existing algorithms, such as modified hybrid forms [20][21][22][23][24][25][26][27][28][29][30][31][32] and parallel running methods [33][34][35][36][37][38], with the aim of getting better optimization performances.…”