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The FPGA implementation of lattice-ladder multilayer perceptron with its training algorithm seems attractive, however there is a lack of experimental results on its efficiency. The main aim of this investigation was to optimize the latency and DSP block usage for the normalized lattice-ladder neuron (LLN) and its simple gradient training algorithm implementation on FPGA. Four alternative regressor...
Describing an Artificial Neural Network (ANN) using VHDL allows a further implementation of such a system on FPGA. Indeed, the principal point of using FPGA for ANNs is flexibility that gives it an advantage toward other systems like ASICS which are entirely dedicated to one unique architecture and allowance to parallel programming, which is inherent to ANN calculation system and one of their advantages...
In this paper we propose usage of neural networks in the field of meteorology especially to detect ice formation on roads. Used algorithm for building and training network is based on the road and air conditions which define ice formation on road's surface. The ability of self and supervised learning are both used to solve this problem. We used VHDL to build proposed neural network as a part of the...
Based on discussions of the hardware implementations of artificial neural network (ANN), the single layer architecture to FPGA of the back propagation artificial neural network (BP ANN) is proposed. To construct the single layer architecture, the computing blocks of BP ANN are presented. The construction of the single layer architecture is described. According the experiments of the implementation...
An accurate method for feature extraction based on FPGA (Field Programmable Gate Arrays) implementation is proposed in this paper. The specific application is offline Farsi handwritten digit recognition. The classification is based on a simple two layer MLP (Multi Layer Perceptron). This method of feature extraction is appropriate for FPGA implementation as it can be implemented only with add and...
Restricted Boltzmann machines (RBMs)- the building block for newly popular deep belief networks (DBNs) - are a promising new tool for machine learning practitioners. However, future research in applications of DBNs is hampered by the considerable computation that training requires. In this paper, we describe a novel architecture and FPGA implementation that accelerates the training of general RBMs...
A binary self organizing map (SOM) has been designed and implemented on a field programmable gate array (FPGA) chip. A novel learning algorithm which takes binary inputs and maintains tri-state weights is presented. The binary SOM has the capability of recognizing binary input sequences after training. A novel tri-state rule is used in updating the network weights during the training phase. The rule...
FPGA implementation of artificial neural networks has been an active research line in past years. ANNs because of their high capabilities in pattern recognition, classification and parallel processing are extensively used in several fields. In this paper, a new method to FPGA implementation of a multi layer perceptron neural network is introduced. The obtained network is used to install a hardware...
A primitive gas recognition system which can discriminate limited species of industrial gas was designed and simulated. The dasiaelectronic nosepsila consists of an array of 8 micro-hotplate based SnO2 thin film gas sensors with different selectivity patterns, signal collecting unit and a signal pattern recognition and decision part in programmable logic device chip. BP (back propagation) neural networks...
Neural networks are widely used in pattern recognition, security applications and robot control. We propose a hardware architecture system; using Tiny Neural Networks (TNN) specialized in image recognition. The generic TNN architecture allows expandability by means of mapping several Basic units (layers) and dynamic reconfiguration; depending on the application specific demands. One of the most important...
We have constructed a FPGA-based ldquoearly neural circuit simulatorrdquo to model the first two stages of stimulus encoding and processing in the rat whisker system. Rats use tactile input from their whiskers to extract object features such as size and shape. We use the simulator to examine the plausibility of the hypothesis that neural circuits in the ratpsilas brain compute gradients of radial...
In this paper, a neural network (NN) for peak power reduction of orthogonal frequency-division multiplexing (OFDM) signals is improved in order to suppress its computational complexity. Numerical experiments show that the proposed NN has less computational complexity than the conventional one. The number of IFFT in NN can be reduced to half, and the computational time can be suppressed by 32.7%. From...
This paper analyzes the criteria for the direct correspondence between a deterministic neural network and its stochastic counterpart, and presents the guidelines that have been derived to establish such a correspondence during the design of a neural network application. In particular, the role of the slope and bias of the neuron activation function and that of the noise of its output have been addressed,...
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