Low-cost hardware implementations for discrete-time spiking neural networks

In this paper, both GPU (Graphing Processing Unit) based and FPGA (Field Programmable Gate Array) based hardware implementations for a discrete-time spiking neuron model are presented. This generalized model is highly adapted for large scale neural network implementations, since its dynamics are entirely represented by a spike train (binary code). This means that at microscopic scale the membrane potentials have a one-to-one correspondence with the spike train, in the asymptotic dynamics. This model also permit us to reproduce complex spiking dynamics such as those obtained with general Integrate-and-Fire (gIF) models. The FPGA design has been coded in Handel-C and VHDL and has been based on a fixed-point reconfigurable architecture, while the GPU spiking neuron kernel has been coded using C++ and CUDA. Numerical verifications are provided

A Review of Surrogate Assisted Multiobjective Evolutionary Algorithms

Multiobjective evolutionary algorithms have incorporated surrogate models in order to reduce the number of required evaluations to approximate the Pareto front of computationally expensive multiobjective optimization problems. Currently, few works have reviewed the state of the art in this topic. However, the existing reviews have focused on classifying the evolutionary multiobjective optimization algorithms with respect to the type of underlying surrogate model. In this paper, we center our focus on classifying multiobjective evolutionary algorithms with respect to their integration with surrogate models. This interaction has led us to classify similar approaches and identify advantages and disadvantages of each class

A Review of Surrogate Assisted Multiobjective Evolutionary Algorithms

Multiobjective evolutionary algorithms have incorporated surrogate models in order to reduce the number of required evaluations to approximate the Pareto front of computationally expensive multiobjective optimization problems. Currently, few works have reviewed the state of the art in this topic. However, the existing reviews have focused on classifying the evolutionary multiobjective optimization algorithms with respect to the type of underlying surrogate model. In this paper, we center our focus on classifying multiobjective evolutionary algorithms with respect to their integration with surrogate models. This interaction has led us to classify similar approaches and identify advantages and disadvantages of each class

Low-cost hardware implementations for discrete-time spiking neural networks

In this paper, both GPU (Graphing Processing Unit) based and FPGA (Field Programmable Gate Array) based hardware implementations for a discrete-time spiking neuron model are presented. This generalized model is highly adapted for large scale neural network implementations, since its dynamics are entirely represented by a spike train (binary code). This means that at microscopic scale the membrane potentials have a one-to-one correspondence with the spike train, in the asymptotic dynamics. This model also permit us to reproduce complex spiking dynamics such as those obtained with general Integrate-and-Fire (gIF) models. The FPGA design has been coded in Handel-C and VHDL and has been based on a fixed-point reconfigurable architecture, while the GPU spiking neuron kernel has been coded using C++ and CUDA. Numerical verifications are provided

Configurable Embedded CPG-Based Control for Robot Locomotion

Recently, the development of intelligent robots has benefited from a deeper understanding of the biomechanics and neurology of biological systems. Researchers have proposed the concept of Central Pattern Generators (CPGs) as a mechanism for generating an efficient control strategy for legged robots based on biological locomotion principles. Although many studies have aimed to develop robust legged locomotion controllers, relatively few of them have focused on adopting the technology for fully practical embedded hardware implementations. In this contribution, a reconfigurable hardware implementation of a CPG-based controller which is able to generate several gaits for quadruped and hexapod robots is presented. The proposed implementation is modular and configurable in order to scale up to legged robots with different degrees of freedom. Experimental results for embedded Field Programmable Gate Array (FPGA) implementations for quadruped and hexapod robot controllers are presented and analysed

Perception-driven adaptive CPG-based locomotion for hexapod robots

International audienceAccording to neurobiological studies, rhythmic motion in animals is controlled by neural circuits known as central pattern gener- ators (CPGs), which are robust against transient perturbations. Yet, CPGs can integrate sensory feedback that potentially enables adaptive locomotion solutions. Despite of previous works, the construction of practical embedded neuromorphic locomotion sys- tems exhibiting similar properties and organization observed in CPGs is still reduced. In this paper a CPG-based control strategy able to modulate motion speed and manage smoothly gait transitions in hexapod robots according to visual information is proposed. Fuzzy logic and finite state machines are the base of the proposed integration mechanism used to map perception into locomotion parameters according to a sensed situation. A vision sensor is integrated in the CPG-based control loop to provide feedback in obstacle avoidance and target tracking behaviors within simplified unknown environments. Experimental results using an hexapod robot confirm both the effectiveness of the proposed control strategy and its use as a experimental embedded platform to investigate further adaptive locomotion, particularly about ways that biological systems fuse information from visual cues to adapt locomotion

Parallel Processor for 3D Recovery from Optical Flow

3D recovery from motion has received a major effort in computer vision systems in the recent years. The main problem lies in the number of operations and memory accesses to be performed by the majority of the existing techniques when translated to hardware or software implementations. This paper proposes a parallel processor for 3D recovery from optical flow. Its main feature is the maximum reuse of data and the low number of clock cycles to calculate the optical flow, along with the precision with which 3D recovery is achieved. The results of the proposed architecture as well as those from processor synthesis are presented

R

We propose to couple the R2 performance measure and Particle Swarm Optimization in order to handle multi/many-objective problems. Our proposal shows that through a well-designed interaction process we could maintain the metaheuristic almost inalterable and through the R2 performance measure we did not use neither an external archive nor Pareto dominance to guide the search. The proposed approach is validated using several test problems and performance measures commonly adopted in the specialized literature. Results indicate that the proposed algorithm produces results that are competitive with respect to those obtained by four well-known MOEAs. Additionally, we validate our proposal in many-objective optimization problems. In these problems, our approach showed its main strength, since it could outperform another well-known indicator-based MOEA