1,364 research outputs found
Hardware Accelerator of Cartesian Genetic Programming with Multiple Fitness Units
A new accelerator of Cartesian genetic programming is presented in this paper. The accelerator is completely implemented in a single FPGA. The proposed architecture contains multiple instances of virtual reconfigurable circuit to evaluate several candidate solutions in parallel. An advanced memory organization was developed to achieve the maximum throughput of processing. The search algorithm is implemented using the on-chip PowerPC processor. In the benchmark problem (image filter evolution) the proposed platform provides a significant speedup (170) in comparison with a highly optimized software implementation. Moreover, the accelerator is 8 times faster than previous FPGA accelerators of image filter evolution
A Genetic Programming Approach to Designing Convolutional Neural Network Architectures
The convolutional neural network (CNN), which is one of the deep learning
models, has seen much success in a variety of computer vision tasks. However,
designing CNN architectures still requires expert knowledge and a lot of trial
and error. In this paper, we attempt to automatically construct CNN
architectures for an image classification task based on Cartesian genetic
programming (CGP). In our method, we adopt highly functional modules, such as
convolutional blocks and tensor concatenation, as the node functions in CGP.
The CNN structure and connectivity represented by the CGP encoding method are
optimized to maximize the validation accuracy. To evaluate the proposed method,
we constructed a CNN architecture for the image classification task with the
CIFAR-10 dataset. The experimental result shows that the proposed method can be
used to automatically find the competitive CNN architecture compared with
state-of-the-art models.Comment: This is the revised version of the GECCO 2017 paper. The code of our
method is available at https://github.com/sg-nm/cgp-cn
Accelerating FPGA-based evolution of wavelet transform filters by optimized task scheduling
Adaptive embedded systems are required in various applications. This work addresses these needs in the
area of adaptive image compression in FPGA devices. A simplified version of an evolution strategy is utilized
to optimize wavelet filters of a Discrete Wavelet Transform algorithm. We propose an adaptive image compression system in FPGA where optimized memory architecture, parallel processing and optimized task scheduling allow reducing the time of evolution. The proposed solution has been extensively evaluated in terms of the quality of compression as well as the processing time. The proposed architecture
reduces the time of evolution by 44% compared to our previous reports while maintaining the quality of compression unchanged with respect to existing implementations. The system is able to find an
optimized set of wavelet filters in less than 2 min whenever the input type of data changes
Neuroimage processing on GPU using CUDA
As time has passed, the general purpose programming paradigm has
evolved, producing different hardware architectures whose characteristics
differ widely. In this work, we are going to demonstrate, through different
applications belonging to the field of Image Processing, the existing
difference between three Nvidia hardware platforms: two of them belong to
the GeForce graphics cards series, the GTX 480 and the GTX 980 and one of
the low consumption platforms which purpose is to allow the execution of
embedded applications as well as providing an extreme efficiency: the Jetson
TK1.
With respect to the test applications we will use five examples from Nvidia
CUDA Samples. These applications are directly related to Image Processing,
as the algorithms they use are similar to those from the field of medical image
registration. After the tests, it will be proven that GTX 980 is both the device
with the highest computational power and the one that has greater
consumption, it will be seen that Jetson TK1 is the most efficient platform, it
will be shown that GTX 480 produces more heat than the others and we will
learn other effects produced by the existing difference between the
architecture of the devices
EIGEN: Ecologically-Inspired GENetic Approach for Neural Network Structure Searching from Scratch
Designing the structure of neural networks is considered one of the most
challenging tasks in deep learning, especially when there is few prior
knowledge about the task domain. In this paper, we propose an
Ecologically-Inspired GENetic (EIGEN) approach that uses the concept of
succession, extinction, mimicry, and gene duplication to search neural network
structure from scratch with poorly initialized simple network and few
constraints forced during the evolution, as we assume no prior knowledge about
the task domain. Specifically, we first use primary succession to rapidly
evolve a population of poorly initialized neural network structures into a more
diverse population, followed by a secondary succession stage for fine-grained
searching based on the networks from the primary succession. Extinction is
applied in both stages to reduce computational cost. Mimicry is employed during
the entire evolution process to help the inferior networks imitate the behavior
of a superior network and gene duplication is utilized to duplicate the learned
blocks of novel structures, both of which help to find better network
structures. Experimental results show that our proposed approach can achieve
similar or better performance compared to the existing genetic approaches with
dramatically reduced computation cost. For example, the network discovered by
our approach on CIFAR-100 dataset achieves 78.1% test accuracy under 120 GPU
hours, compared to 77.0% test accuracy in more than 65, 536 GPU hours in [35].Comment: CVPR 201
Real-Time Automatic Object Classification and Tracking using Genetic Programming and NVIDIA R CUDA TM
Genetic Programming (GP) is a widely used methodology for solving various computational problems. GP's problem solving ability is usually hindered by its long execution times. In this thesis, GP is applied toward real-time computer vision. In particular, object classification and tracking using a parallel GP system is discussed. First, a study of suitable GP languages for object classification is presented. Two main GP approaches for visual pattern classification, namely the block-classifiers and the pixel-classifiers, were studied. Results showed that the pixel-classifiers generally performed better. Using these results, a suitable language was selected for the real-time implementation. Synthetic video data was used in the experiments. The goal of the experiments was to evolve a unique classifier for each texture pattern that existed in the video. The experiments revealed that the system was capable of correctly tracking the textures in the video. The performance of the system was on-par with real-time requirements
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