By Ricardo Lourenço, Nuno Lourenço, Nuno Horta
This paintings addresses the examine and improvement of an leading edge optimization kernel utilized to analog built-in circuit (IC) layout. rather, this works describes the differences contained in the AIDA Framework, an digital layout automation framework totally built by means of on the built-in Circuits Group-LX of the Instituto de Telecomunicações, Lisbon. It focusses on AIDA-CMK, via improving AIDA-C, that is the circuit optimizer section of AIDA, with a brand new multi-objective multi-constraint optimization module that constructs a base for a number of set of rules implementations. The proposed resolution implements 3 techniques to multi-objective multi-constraint optimization, specifically, an evolutionary technique with NSGAII, a swarm intelligence method with MOPSO and stochastic hill mountaineering procedure with MOSA. in addition, the applied constitution permits the simple hybridization among kernels reworking the former easy NSGAII optimization module right into a extra developed and flexible module aiding a number of unmarried and multi-kernel algorithms. the 3 multi-objective optimization methods have been proven with CEC2009 benchmarks to restricted multi-objective optimization and proven with genuine analog IC layout difficulties. The accomplished effects have been in comparison by way of functionality, utilizing statistical effects acquired from a number of self sufficient runs. ultimately, a few hybrid methods have been additionally experimented, giving a foretaste to a variety of possibilities to discover in destiny work.
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Additional resources for AIDA-CMK: Multi-Algorithm Optimization Kernel Applied to Analog IC Sizing
Each chromosome has an associated value corresponding to the ﬁtness of the solution it represents. The ﬁtness should correspond to an evaluation of how good the candidate solution is. Selection compares each individual in the population by using a ﬁtness function. The new individuals’ ﬁtness is evaluated and, then, they are ranked together with the parents. The ﬁttest individuals are selected as the new parents, and the less ﬁt discarded. 1. NSGA-II uses Pareto dominance concepts to sort the multi-objective solutions.
The genetic algorithm starts by generating an initial population of chromosomes, the initial parents. This ﬁrst population must offer a wide diversity of genetic materials. The gene pool should be as large as possible so that any solution of the search space can be engendered but generally, the initial population is generated randomly. Then, the genetic algorithm evolves the solutions by applying the genetic operators and then selecting the next parents. The process is repeated until the convergence or ending criterion is reached.
Despite the output does not consider the limitations imposed by the extreme variations of process and environment parameters, it is useful to the circuit designer to perform tradeoff analysis. A critical problem in analog IC design is Process, supply Voltage and Temperature variability (PVT). , devices designed to be equal are different after production due to manufacturing mismatches and are solved by robust circuit design. , the vast majority of the fabricated circuits will work according to the speciﬁcations, special techniques are employed.
AIDA-CMK: Multi-Algorithm Optimization Kernel Applied to Analog IC Sizing by Ricardo Lourenço, Nuno Lourenço, Nuno Horta