Computational system modelling is full of ambiguous situations, wherein the designer cannot decid... more Computational system modelling is full of ambiguous situations, wherein the designer cannot decide, with precision, what should be the outcome of the system. In [17], L. Zadeh introduced for the first time the concept of fuzziness as opposed to crispiness in data sets. When he invented fuzzy sets together with the underlying theory, Zadeh’s main concern was to reduce system complexity and provide designer with a new computing paradigm that allow approximate results. Whenever there is uncertainty, fuzzy logic together with approximate reasoning apply. Fuzzy logic and approximate reasoning [18, 19] can be used in system modelling and control as well as data clustering and prediction [13], to name only few appropriate utilisations. Furthermore, they can be applied to any discipline such as finance [4], image processing [7,16], temperature and pressure control [11, 22], robot control [9, 14], etc.
In this paper, we propose an overall architecture for hardware implementation of genetic algorith... more In this paper, we propose an overall architecture for hardware implementation of genetic algorithms. The proposed architecture is independent of such specifics. It implements the fitness computation using a neural networks.
Euromicro Symposium on Digital System Design, 2003. Proceedings., 2003
In this paper, we propose reconfigurable, low-cost and readily available hardware architecture fo... more In this paper, we propose reconfigurable, low-cost and readily available hardware architecture for an artificial neuron. This is used to build a feed-forward artificial neural network. For this purpose, we use field-programmable gate arrays, i.e. FPGAs. However, as the state-of-the-art FPGAs still lack the gate density necessary to the implementation of large neural networks of thousands of neurons, we use a stochastic process to implement the computation performed by a neuron. The multiplication and addition of stochastic values is simply implemented by an ensemble of XNOR and AND gates respectively.
2013 IEEE 4th Latin American Symposium on Circuits and Systems (LASCAS), 2013
ABSTRACT Particle Swarm Optimization (PSO) is an evolutionary heuristics-based method used for co... more ABSTRACT Particle Swarm Optimization (PSO) is an evolutionary heuristics-based method used for continuous function optimization. Compared to existing stochastic methods, PSO is very robust. Nevertheless, for real-world optimizations, it requires a high computational effort. In general, parallel implementations of PSO provide better performance. However, this depends heavily on the number and characteristics of the exploited processors. With the advent and large availability of Graphics Processing Units (GPUs) and the development and straightforward applicability of the Compute Unified Device Architecture platform (CUDA), several applications have benefited from the reduction of the execution time, exploiting massive parallelism. In this paper, we propose an alternative algorithm to massively parallelize the PSO algorithm and mapped it onto a GPU-based architecture. The algorithm focuses on the work done with respect to each of the problem dimension and does it in parallel.
Synchronous finite state machines are very important for digital sequential systems. Among other ... more Synchronous finite state machines are very important for digital sequential systems. Among other important aspects, they represent a powerful way for synchronising hardware components so that these components may cooperate adequately in the fulfilment of the main objective. In this paper, we propose to use an evolutionary methodology inspired from quantum computation to yield a concise and efficient evolvable hardware
ABSTRACT This paper proposes a distributed control algorithm to im- plement dynamic task allocati... more ABSTRACT This paper proposes a distributed control algorithm to im- plement dynamic task allocation in a swarm robotics environment. In this context, each robot that integrates the swarm must run the algorithm periodically in order to control the underlying actions and decisions. The algorithm was implemented and extensively tested. The corresponding performance and effectiveness are promising.
Euromicro Symposium on Digital System Design, 2004. DSD 2004., 2004
... Nedjah Department of de Systems Engineering and Computation, Faculty of Engineering, State Un... more ... Nedjah Department of de Systems Engineering and Computation, Faculty of Engineering, State University of Rio de Janeiro, Brazil (ldmm|nadia)@eng.uerj ... by a state machine, which has 6 states defined as follows: S0: initialize the system; if start signal is activated then go to S1 ...
International Journal of Modelling and Simulation, 2006
Abstract Modular multiplication is fundamental to several public-key cryptography systems such as... more Abstract Modular multiplication is fundamental to several public-key cryptography systems such as the RSA encryption system. It is also the most dominant part of the computation performed in such systems. The operation is time consuming for large operands. This paper examines the characteristics of yet another architecture to implement modular multiplication. It interleaves Booth's multiplication and Barrett's reduction methods. The functionality of the Booth-Barrett proposed multiplier is accessed through simulation and its performance is evaluated by comparing it to a Montgomery-based modular multiplier. The comparative analysis shows that the proposed multiplier performs considerably better than the Montgomery multiplier when the operand size is smaller than 512. However, when this size gets closer to 1024, the performance of the Booth-Barrett multiplier degrades relative to that of the Montgomery-based modular multiplier.
Page 1. 4 Evolutionary Pattern Matching Using Genetic Programming Nadia Nedjah1 and Luiza de Mace... more Page 1. 4 Evolutionary Pattern Matching Using Genetic Programming Nadia Nedjah1 and Luiza de Macedo Mourelle2 1 Department of ... Heidelberg 2006 Page 2. 82 Nadia Nedjah and Luiza de Macedo Mourelle 4.1 Introduction Pattern ...
Proceedings. 15th Symposium on Integrated Circuits and Systems Design
... Nadia Nedjah and Luiza de Macedo Mourelle Department of de Systems Engineering and Computatio... more ... Nadia Nedjah and Luiza de Macedo Mourelle Department of de Systems Engineering and Computation, Faculty of Engineering, State University of Rio de Janeiro (ldmm | nadia)@eng. uerj.br Abstract ... S0: initialisation of the state machine; go to S1; S1: load multiplicand and ...
2010 15th CSI International Symposium on Computer Architecture and Digital Systems, 2010
Most of cryptographic systems are based on modular exponentiation. It is performed using successi... more Most of cryptographic systems are based on modular exponentiation. It is performed using successive modular multiplications. One way of improving the throughput of a cryptographic system implementation is reducing the number of the required modular multiplications. Existing methods attempt to reduce this number by partitioning the exponent in constant or variable size windows. In this paper, in the purpose of
Network-on-chip (NoC) are considered the next generation of communication infrastructure for a mu... more Network-on-chip (NoC) are considered the next generation of communication infrastructure for a multiprocessors system-on-chip (MPSoCs). In the platform-based methodology, an application is implemented by a set of collaborating intellectual properties (IPs) blocks. In this paper, we use NSGA-II and microGA to yield efficient topological pre-selected sets IPs on the tiles of a mesh-based NoC. Each IP is associated with a
2014 IEEE Biennial Congress of Argentina (ARGENCON), 2014
ABSTRACT This paper proposes and tests a clustering technique of swarm robots into ζ clases. Base... more ABSTRACT This paper proposes and tests a clustering technique of swarm robots into ζ clases. Based only on local information coming from neighboring robots and the distribution of virtual tokens in the system, the robots of the swarm can be grouped into different classes. The proposed technique acts in a distributed manner and without any global knowledge or movement of the robots. Depending on the amount and weight of the tokens available in the system, robots exchange information to reach a token uniform distribution. The clustering technique is inspired by the process of solids or liquids settling. Using information gathered from neighboring robots, a token density is computed. As a result, the tokens with higher weights form a cluster first, shifting those of lower weight until they form differentiated bands for each group, thus completing the clustering of the swarm robots.
The International Journal of Computers, Systems and Signal - IJCSS, 2005
In this paper, we focus on engineering Pareto-optimal digital circuits given the expected input/o... more In this paper, we focus on engineering Pareto-optimal digital circuits given the expected input/output behaviour with a minimal design effort. The design objectives to be minimised are: hardware area, response time and power consumption. We do so using the Strength Pareto Evolutionary Algorithms. This is novel application of multi-objective optimisation to circuit design. The performance and quality of the circuits evolved for some benchmarks are presented then compared to those of single objective genetic algorithms as well as to the circuits obtained by human designers. We show that the evolutionary hardware is far better with respect to all objectives than those designed using traditional methods. Keywords: Evolvable hardware, Multi-objective optimisation, Digital circuits, SPEA.
In this paper, we propose a massively parallel architecture for hardware implementation of geneti... more In this paper, we propose a massively parallel architecture for hardware implementation of genetic algorithms. This 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. Last but not least, we demonstrate the characteristics of the proposed hardware and compare it to existing ones.
International Journal of Innovative Computing and Applications, 2007
... Product ciphers use alternating substitution and transposition phases to achieve both confusi... more ... Product ciphers use alternating substitution and transposition phases to achieve both confusion and diffusion ... the less auto-correlated the S-box is, the more resilient the cryptosystem that uses ... the Nash equilibrium point should be reached when no player can improve further the ...
Computational system modelling is full of ambiguous situations, wherein the designer cannot decid... more Computational system modelling is full of ambiguous situations, wherein the designer cannot decide, with precision, what should be the outcome of the system. In [17], L. Zadeh introduced for the first time the concept of fuzziness as opposed to crispiness in data sets. When he invented fuzzy sets together with the underlying theory, Zadeh’s main concern was to reduce system complexity and provide designer with a new computing paradigm that allow approximate results. Whenever there is uncertainty, fuzzy logic together with approximate reasoning apply. Fuzzy logic and approximate reasoning [18, 19] can be used in system modelling and control as well as data clustering and prediction [13], to name only few appropriate utilisations. Furthermore, they can be applied to any discipline such as finance [4], image processing [7,16], temperature and pressure control [11, 22], robot control [9, 14], etc.
In this paper, we propose an overall architecture for hardware implementation of genetic algorith... more In this paper, we propose an overall architecture for hardware implementation of genetic algorithms. The proposed architecture is independent of such specifics. It implements the fitness computation using a neural networks.
Euromicro Symposium on Digital System Design, 2003. Proceedings., 2003
In this paper, we propose reconfigurable, low-cost and readily available hardware architecture fo... more In this paper, we propose reconfigurable, low-cost and readily available hardware architecture for an artificial neuron. This is used to build a feed-forward artificial neural network. For this purpose, we use field-programmable gate arrays, i.e. FPGAs. However, as the state-of-the-art FPGAs still lack the gate density necessary to the implementation of large neural networks of thousands of neurons, we use a stochastic process to implement the computation performed by a neuron. The multiplication and addition of stochastic values is simply implemented by an ensemble of XNOR and AND gates respectively.
2013 IEEE 4th Latin American Symposium on Circuits and Systems (LASCAS), 2013
ABSTRACT Particle Swarm Optimization (PSO) is an evolutionary heuristics-based method used for co... more ABSTRACT Particle Swarm Optimization (PSO) is an evolutionary heuristics-based method used for continuous function optimization. Compared to existing stochastic methods, PSO is very robust. Nevertheless, for real-world optimizations, it requires a high computational effort. In general, parallel implementations of PSO provide better performance. However, this depends heavily on the number and characteristics of the exploited processors. With the advent and large availability of Graphics Processing Units (GPUs) and the development and straightforward applicability of the Compute Unified Device Architecture platform (CUDA), several applications have benefited from the reduction of the execution time, exploiting massive parallelism. In this paper, we propose an alternative algorithm to massively parallelize the PSO algorithm and mapped it onto a GPU-based architecture. The algorithm focuses on the work done with respect to each of the problem dimension and does it in parallel.
Synchronous finite state machines are very important for digital sequential systems. Among other ... more Synchronous finite state machines are very important for digital sequential systems. Among other important aspects, they represent a powerful way for synchronising hardware components so that these components may cooperate adequately in the fulfilment of the main objective. In this paper, we propose to use an evolutionary methodology inspired from quantum computation to yield a concise and efficient evolvable hardware
ABSTRACT This paper proposes a distributed control algorithm to im- plement dynamic task allocati... more ABSTRACT This paper proposes a distributed control algorithm to im- plement dynamic task allocation in a swarm robotics environment. In this context, each robot that integrates the swarm must run the algorithm periodically in order to control the underlying actions and decisions. The algorithm was implemented and extensively tested. The corresponding performance and effectiveness are promising.
Euromicro Symposium on Digital System Design, 2004. DSD 2004., 2004
... Nedjah Department of de Systems Engineering and Computation, Faculty of Engineering, State Un... more ... Nedjah Department of de Systems Engineering and Computation, Faculty of Engineering, State University of Rio de Janeiro, Brazil (ldmm|nadia)@eng.uerj ... by a state machine, which has 6 states defined as follows: S0: initialize the system; if start signal is activated then go to S1 ...
International Journal of Modelling and Simulation, 2006
Abstract Modular multiplication is fundamental to several public-key cryptography systems such as... more Abstract Modular multiplication is fundamental to several public-key cryptography systems such as the RSA encryption system. It is also the most dominant part of the computation performed in such systems. The operation is time consuming for large operands. This paper examines the characteristics of yet another architecture to implement modular multiplication. It interleaves Booth's multiplication and Barrett's reduction methods. The functionality of the Booth-Barrett proposed multiplier is accessed through simulation and its performance is evaluated by comparing it to a Montgomery-based modular multiplier. The comparative analysis shows that the proposed multiplier performs considerably better than the Montgomery multiplier when the operand size is smaller than 512. However, when this size gets closer to 1024, the performance of the Booth-Barrett multiplier degrades relative to that of the Montgomery-based modular multiplier.
Page 1. 4 Evolutionary Pattern Matching Using Genetic Programming Nadia Nedjah1 and Luiza de Mace... more Page 1. 4 Evolutionary Pattern Matching Using Genetic Programming Nadia Nedjah1 and Luiza de Macedo Mourelle2 1 Department of ... Heidelberg 2006 Page 2. 82 Nadia Nedjah and Luiza de Macedo Mourelle 4.1 Introduction Pattern ...
Proceedings. 15th Symposium on Integrated Circuits and Systems Design
... Nadia Nedjah and Luiza de Macedo Mourelle Department of de Systems Engineering and Computatio... more ... Nadia Nedjah and Luiza de Macedo Mourelle Department of de Systems Engineering and Computation, Faculty of Engineering, State University of Rio de Janeiro (ldmm | nadia)@eng. uerj.br Abstract ... S0: initialisation of the state machine; go to S1; S1: load multiplicand and ...
2010 15th CSI International Symposium on Computer Architecture and Digital Systems, 2010
Most of cryptographic systems are based on modular exponentiation. It is performed using successi... more Most of cryptographic systems are based on modular exponentiation. It is performed using successive modular multiplications. One way of improving the throughput of a cryptographic system implementation is reducing the number of the required modular multiplications. Existing methods attempt to reduce this number by partitioning the exponent in constant or variable size windows. In this paper, in the purpose of
Network-on-chip (NoC) are considered the next generation of communication infrastructure for a mu... more Network-on-chip (NoC) are considered the next generation of communication infrastructure for a multiprocessors system-on-chip (MPSoCs). In the platform-based methodology, an application is implemented by a set of collaborating intellectual properties (IPs) blocks. In this paper, we use NSGA-II and microGA to yield efficient topological pre-selected sets IPs on the tiles of a mesh-based NoC. Each IP is associated with a
2014 IEEE Biennial Congress of Argentina (ARGENCON), 2014
ABSTRACT This paper proposes and tests a clustering technique of swarm robots into ζ clases. Base... more ABSTRACT This paper proposes and tests a clustering technique of swarm robots into ζ clases. Based only on local information coming from neighboring robots and the distribution of virtual tokens in the system, the robots of the swarm can be grouped into different classes. The proposed technique acts in a distributed manner and without any global knowledge or movement of the robots. Depending on the amount and weight of the tokens available in the system, robots exchange information to reach a token uniform distribution. The clustering technique is inspired by the process of solids or liquids settling. Using information gathered from neighboring robots, a token density is computed. As a result, the tokens with higher weights form a cluster first, shifting those of lower weight until they form differentiated bands for each group, thus completing the clustering of the swarm robots.
The International Journal of Computers, Systems and Signal - IJCSS, 2005
In this paper, we focus on engineering Pareto-optimal digital circuits given the expected input/o... more In this paper, we focus on engineering Pareto-optimal digital circuits given the expected input/output behaviour with a minimal design effort. The design objectives to be minimised are: hardware area, response time and power consumption. We do so using the Strength Pareto Evolutionary Algorithms. This is novel application of multi-objective optimisation to circuit design. The performance and quality of the circuits evolved for some benchmarks are presented then compared to those of single objective genetic algorithms as well as to the circuits obtained by human designers. We show that the evolutionary hardware is far better with respect to all objectives than those designed using traditional methods. Keywords: Evolvable hardware, Multi-objective optimisation, Digital circuits, SPEA.
In this paper, we propose a massively parallel architecture for hardware implementation of geneti... more In this paper, we propose a massively parallel architecture for hardware implementation of genetic algorithms. This 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. Last but not least, we demonstrate the characteristics of the proposed hardware and compare it to existing ones.
International Journal of Innovative Computing and Applications, 2007
... Product ciphers use alternating substitution and transposition phases to achieve both confusi... more ... Product ciphers use alternating substitution and transposition phases to achieve both confusion and diffusion ... the less auto-correlated the S-box is, the more resilient the cryptosystem that uses ... the Nash equilibrium point should be reached when no player can improve further the ...
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