Mediaspace scheduled maintenance: Aug 25, 2026 07:00 - 12:00 AM. During this time, videos will be temporarily unavailable. Check status updates.

Lecture

Reconfigurable Architecture: Static Interconnect Fault-Tolerant

Description

This lecture focuses on a reconfiguration architecture for semiconductor devices, addressing design variability and low power circuits. The instructor presents a matrix of statically interconnected cells, allowing for fault-tolerant computing and data routing. Tools like genetic algorithms are used to optimize the mapping of applications onto the cells, while controllers manage defects and configurations. Results show trade-offs between lightweight and heavyweight controllers, with a focus on correction success rates and design complexity.

Official source

Related concepts (9)

Genetic algorithm

In computer science and operations research, a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on biologically inspired operators such as mutation, crossover and selection. Some examples of GA applications include optimizing decision trees for better performance, solving sudoku puzzles, hyperparameter optimization, causal inference, etc.

Chromosome (genetic algorithm)

In genetic algorithms (GA), or more general, evolutionary algorithms (EA), a chromosome (also sometimes called a genotype) is a set of parameters which define a proposed solution of the problem that the evolutionary algorithm is trying to solve. The set of all solutions, also called individuals according to the biological model, is known as the population. The genome of an individual consists of one, more rarely of several, chromosomes and corresponds to the genetic representation of the task to be solved.

Crossover (genetic algorithm)

In genetic algorithms and evolutionary computation, crossover, also called recombination, is a genetic operator used to combine the genetic information of two parents to generate new offspring. It is one way to stochastically generate new solutions from an existing population, and is analogous to the crossover that happens during sexual reproduction in biology. Solutions can also be generated by cloning an existing solution, which is analogous to asexual reproduction. Newly generated solutions may be mutated before being added to the population.

Memetic algorithm

A memetic algorithm (MA) in computer science and operations research, is an extension of the traditional genetic algorithm (GA) or more general evolutionary algorithm (EA). It may provide a sufficiently good solution to an optimization problem. It uses a suitable heuristic or local search technique to improve the quality of solutions generated by the EA and to reduce the likelihood of premature convergence. Memetic algorithms represent one of the recent growing areas of research in evolutionary computation.

Semiconductor device

A semiconductor device is an electronic component that relies on the electronic properties of a semiconductor material (primarily silicon, germanium, and gallium arsenide, as well as organic semiconductors) for its function. Its conductivity lies between conductors and insulators. Semiconductor devices have replaced vacuum tubes in most applications. They conduct electric current in the solid state, rather than as free electrons across a vacuum (typically liberated by thermionic emission) or as free electrons and ions through an ionized gas.

Related lectures (32)

Semiconductor Devices II: Defects Engineering

Covers the analysis of measurements and defects engineering in semiconductor devices, including density of states and defect probing.

Semiconductor Devices II: Contact Resistance Modeling

Explores contact resistance modeling in semiconductor devices, focusing on gate voltage calculation and defect analysis.

Cellular Physiology: Neuronal Firing Patterns

Explores recreating neuronal firing patterns through ion channel models and genetic algorithms.

Heuristic Optimization Methods

Explores heuristic optimization methods to find the global optimum efficiently.

Simulation of 2D Field Effect Transistors