A review of bioinspired computer‐aided design tools for hardware design

Abstract

During the last two decades, bioinspired techniques have emerged as a powerful optimization and problem solving tool for a wide range of real world applications. Parallel architectures and computer-aided design tools have been also influenced by this evolutionary fashion. In this paper, we give a broader view of the application of techniques inspired by nature to hardware design and parallel architectures problem solving. Our aim is to furnish an overview of the various bioinspired techniques based tools that have been used so far to solve the problems of automatic hardware design. We can claim that a lot of the approaches found in the literature suffer from a lack of interdisciplinary interaction among researchers of both evolutionary computation and hardware design fields. In addition, we have also detected that some multi-objective problems do not use the appropriate algorithms.Among widely known heuristics, gradient algorithms, such as Hill Climbing, were soon employed. These algorithms start the search process from a single point, stochastically generated from the solution space, and then perform a search process using the direction of the gradient towards the objective function. These algorithms are of an easy implementation and relatively efficient. However, local optima are usually the output and no general procedures exist to know the distance from the global optimum.An improvement over the previous approach was obtained by the simulated annealing (SA) algorithm [2], capable of removing some of the previously described disadvantages. SA begins with a randomly generated candidate solution-a point in the search space-with some specific features, and then, the algorithm proceeds by applying a disturbance to the candidate solution, thus trying to improve it. If the solution improves, it is selected and becomes the next candidate solution; otherwise, a probability decides whether to continue with the initial candidate or with the new one. The probability of accepting a worse solution depends on the system temperature-a fundamental parameter for SA. The system starts with a very high temperature, so most of the worse solutions are accepted at the beginning. The temperature is then lowered gradually; the lower the temperature, the lower the probability of accepting a worse solution. So far, SA has been the most frequently employed heuristic in computer-aided design (CAD) tools.Nevertheless, a new kind of heuristic has gained popularity: bioinspired algorithms (BAs) [3]. Among BAs, evolutionary algorithms (EAs) try to emulate the natural evolution of species, following Darwin's principles of natural selection. One of their main advantages is the employment of a set (population) of candidate solutions (individuals) at the same time, which gives rise to an intrinsically parallel search process. We can also find other bioinspired approaches on the basis of the social behavior of the species such as particle swarm optimization (PSO) [4] or ant colony optimization (ACO) [5] algorithms.This review focuses on the appli...

A Comprehensive Review of Bio-Inspired Optimization Algorithms Including Applications in Microelectronics and Nanophotonics

“…are optimized. Floor Planning and Topology [ 229 , 230 ]: This crucial optimization step includes the placement of the circuit elements on a chip and the routing of their interconnects. This helps minimize crosstalk, avoid breakdowns, minimize leakage currents, ensure more homogeneous heat dissipation, etc.…”

“…Optimal circuit sizing is actually of interest for basically all microelectronic circuits since it helps improve overall performance and enhance circuit reliability. Power Consumption [ 229 ]: Bio-inspired algorithms can be employed to optimize power consumption by minimizing leakage current, optimizing voltage levels and reducing dynamic power dissipation in circuits. This directly improves circuit reliability by avoiding overheating, and it also helps in keeping the power consumption at its minimum, which is of paramount importance for all battery-supplied circuitry.…”

Development of Multiobjective High-Level Synthesis for FPGAs

“…e paper also presented methodologies for multiobjective optimization and a classification of metaheuristics that were used. A review of bioinspired optimization techniques was presented in [21], including a few evolutionary multiobjective approaches.…”

Special issue on parallel architectures and bioinspired algorithm: guest editors message

“…We thus invited Professors Oscar Garnica and Juan Lanchares to work with us with this aim. Therefore, this special issue includes the paper entitled A review of bioinspired CAD tools for parallel architectures and hardware design together with four papers carefully selected for publications, extended versions of Parallel Architectures and Bioinspired Algorithms 2011 workshop's best papers.…”