Reconfigurable Hardware Architecture for Compact and Efficient Stochastic Neuron

Nedjah, Nadia; Mourelle, Luiza Macedo

doi:10.1007/3-540-44869-1_3

Cited by 10 publications

(11 citation statements)

References 5 publications

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“…The main problem of implementing of genetic algorithm is fitness function. For this reason, to calculate it, hardware of neural networks is used [2, 9,10]. Ideal estimation of each individual can be obtained using this network.…”

Section: Overall View Of Proposed Architecturementioning

confidence: 99%

“…For the reason that the fitness function will not be depending on a problem, the neural network is used to estimate fitness measure [10] in this work. In [10] neural network has been implemented using stochastic signals and therefore reduces very significantly the hardware area required for the network.…”

Section: G Fitness Componentsmentioning

confidence: 99%

“…In [10] neural network has been implemented using stochastic signals and therefore reduces very significantly the hardware area required for the network. For the genetic hardware implementation, the number of input neurons is the same as the size of individuals in population.…”

Section: G Fitness Componentsmentioning

confidence: 99%

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New Hardware Engine for New Operating Systems

Ahmadi

Kaveh

2013

ILNS

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Genetic algorithm is a soft computing method that works on set of solutions. These solutions are called chromosome and the best one is the absolute solution of the problem. The main problem of this algorithm is that after passing through some generations, it may be produced some chromosomes that had been produced in some generations ago that causes reducing the convergence speed. From another respective, most of the genetic algorithms are implemented in software and less works have been done on hardware implementation. Our work implements genetic algorithm in hardware that doesn't produce chromosome that have been produced in previous generations. In this work, most of genetic operators are implemented without producing iterative chromosomes and genetic diversity is preserved. Genetic diversity causes that not only don't this algorithm converge to local optimum but also reaching to global optimum. Without any doubts, proposed approach is so faster than software implementations. Evaluation results also show the proposed approach is faster than hardware ones.

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Section: Overall View Of Proposed Architecturementioning

confidence: 99%

Section: G Fitness Componentsmentioning

confidence: 99%

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New Hardware Engine for New Operating Systems

Ahmadi

Kaveh

2013

ILNS

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“…Adders and subtracters can be implemented by an ensemble of multiplexers and multipliers by a series of XOR gates. (For formal proofs on stochastic arithmetic, see [6]. )…”

Section: Overall Architecture For the Hardware Genetic Algorithmmentioning

confidence: 99%

“…The fitness of individual in the generational population is evaluated using hardware neural networks, which take advantage of stochastic representation of signals to reduce the hardware area required [6]. The motivation behind the use of stochastic arithmetic is its simplicity.…”

Section: Overall Architecture For the Hardware Genetic Algorithmmentioning

confidence: 99%

Massively Parallel Hardware Architecture for Genetic Algorithms

Nedjah

Mourelle

8th Euromicro Conference on Digital System Design (DSD'05)

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In this paper, we propose a massively parallel architecture for hardware implementation of genetic algorithms. This is design is quite innovative as it provides a viable solution to the fitness computation problem, which depends heavily on the problem-specific knowledge. The proposed architecture is completely independent of such specifics. It implements the fitness computation using a neural network. The hardware implementation of the used neural network is stochastic and thus minimise the required hardware area without much increase in response time. Finally, we compare the proposed hardware and existing ones.

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Hardware Architecture for Genetic Algorithms

Nedjah

Mourelle

2005

Innovations in Applied Artificial Intelligence

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Reconfigurable Hardware Architecture for Compact and Efficient Stochastic Neuron

Cited by 10 publications

References 5 publications

New Hardware Engine for New Operating Systems

New Hardware Engine for New Operating Systems

Massively Parallel Hardware Architecture for Genetic Algorithms

Hardware Architecture for Genetic Algorithms

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