scholarly journals A Cluster of FPAAs to Recognize Images using Neural Networks

2021 ◽  
Author(s):  
Daniel García Moreno ◽  
Alberto A Del Barrio ◽  
Guillermo Botella ◽  
Jennifer Hasler
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Feedforward Neural Network ◽  
Software Framework ◽  
Use Case ◽  
Reduced Integration ◽  
Main Drawback ◽  
Analog Computing ◽  
Analog Domain

Analog computing has been recovering its relevance in the recent years. FPAAs are the equivalent to FPGAs but in the analog domain. The main drawback of FPAAs is their reduced integration capacity. In order to increase the amount of analog resources, in this paper a cluster of 40 FPAAs is proposed. As a use case, a 19-8-6-4 feedforward Neural Network has been implemented on such cluster. With the help of a DCT-based software framework, this NN is able to classify 28x28 MNIST images. Results show that the analog network is able to obtain almost the same results as the software baseline network.<br>

scholarly journals A Cluster of FPAAs to Recognize Images Using Neural Networks

2021 ◽  
Author(s):  
Daniel García Moreno ◽  
Alberto A Del Barrio ◽  
Guillermo Botella ◽  
Jennifer Hasler
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Feedforward Neural Network ◽  
Software Framework ◽  
Use Case ◽  
Reduced Integration ◽  
Main Drawback ◽  
Analog Computing ◽  
Analog Domain

Analog computing has been recovering its relevance in the recent years. FPAAs are the equivalent to FPGAs but in the analog domain. The main drawback of FPAAs is their reduced integration capacity. In order to increase the amount of analog resources, in this paper a cluster of 40 FPAAs is proposed. As a use case, a 19-8-6-4 feedforward Neural Network has been implemented on such cluster. With the help of a DCT-based software framework, this NN is able to classify 28x28 MNIST images. Results show that the analog network is able to obtain almost the same results as the software baseline network.<br>

scholarly journals A Cluster of FPAAs to Recognize Images Using Neural Networks

2021 ◽  
Author(s):  
Daniel García Moreno ◽  
Alberto A Del Barrio ◽  
Guillermo Botella ◽  
Jennifer Hasler
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Feedforward Neural Network ◽  
Software Framework ◽  
Use Case ◽  
Reduced Integration ◽  
Main Drawback ◽  
Analog Computing ◽  
Analog Domain

Analog computing has been recovering its relevance in the recent years. FPAAs are the equivalent to FPGAs but in the analog domain. The main drawback of FPAAs is their reduced integration capacity. In order to increase the amount of analog resources, in this paper a cluster of 40 FPAAs is proposed. As a use case, a 19-8-6-4 feedforward Neural Network has been implemented on such cluster. With the help of a DCT-based software framework, this NN is able to classify 28x28 MNIST images. Results show that the analog network is able to obtain almost the same results as the software baseline network.<br>

Operational Determination of the Activated Sludge Process Using Neural Networks

1992 ◽  
Vol 26 (9-11) ◽  
pp. 2461-2464 ◽  
Author(s):  
R. D. Tyagi ◽  
Y. G. Du
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Steady State ◽  
Activated Sludge ◽  
Feedforward Neural Network ◽  
Training Data ◽  
Activated Sludge Process

A steady-statemathematical model of an activated sludgeprocess with a secondary settler was developed. With a limited number of training data samples obtained from the simulation at steady state, a feedforward neural network was established which exhibits an excellent capability for the operational prediction and determination.

scholarly journals A Neural Network Decomposition Algorithm for Mapping on Crossbar-Based Computing Systems

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1526 ◽  
Author(s):  
Choongmin Kim ◽  
Jacob A. Abraham ◽  
Woochul Kang ◽  
Jaeyong Chung
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Predictive Performance ◽  
Fixed Number ◽  
Decomposition Algorithm ◽  
Computing Systems ◽  
Network Decomposition ◽  
Von Neumann ◽  
Analog Computing ◽  
The Neural Network

Crossbar-based neuromorphic computing to accelerate neural networks is a popular alternative to conventional von Neumann computing systems. It is also referred as processing-in-memory and in-situ analog computing. The crossbars have a fixed number of synapses per neuron and it is necessary to decompose neurons to map networks onto the crossbars. This paper proposes the k-spare decomposition algorithm that can trade off the predictive performance against the neuron usage during the mapping. The proposed algorithm performs a two-level hierarchical decomposition. In the first global decomposition, it decomposes the neural network such that each crossbar has k spare neurons. These neurons are used to improve the accuracy of the partially mapped network in the subsequent local decomposition. Our experimental results using modern convolutional neural networks show that the proposed method can improve the accuracy substantially within about 10% extra neurons.

scholarly journals Short-Term Load Forecasting Model Based on Quantum Elman Neural Networks

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Zhisheng Zhang ◽  
Wenjie Gong
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Quantum Computation ◽  
Load Forecasting ◽  
Feedforward Neural Network ◽  
Forecasting Model ◽  
Short Term ◽  
Feedback Connection ◽  
Model Based ◽  
Short Term Load Forecasting

Short-term load forecasting model based on quantum Elman neural networks was constructed in this paper. The quantum computation and Elman feedback mechanism were integrated into quantum Elman neural networks. Quantum computation can effectively improve the approximation capability and the information processing ability of the neural networks. Quantum Elman neural networks have not only the feedforward connection but also the feedback connection. The feedback connection between the hidden nodes and the context nodes belongs to the state feedback in the internal system, which has formed specific dynamic memory performance. Phase space reconstruction theory is the theoretical basis of constructing the forecasting model. The training samples are formed by means ofK-nearest neighbor approach. Through the example simulation, the testing results show that the model based on quantum Elman neural networks is better than the model based on the quantum feedforward neural network, the model based on the conventional Elman neural network, and the model based on the conventional feedforward neural network. So the proposed model can effectively improve the prediction accuracy. The research in the paper makes a theoretical foundation for the practical engineering application of the short-term load forecasting model based on quantum Elman neural networks.

A Penalty-Function Approach for Pruning Feedforward Neural Networks

1997 ◽  
Vol 9 (1) ◽  
pp. 185-204 ◽  
Author(s):  
Rudy Setiono
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Penalty Function ◽  
Feedforward Neural Networks ◽  
Feedforward Neural Network ◽  
Function Approach ◽  
Penalty Function Approach

This article proposes the use of a penalty function for pruning feedforward neural network by weight elimination. The penalty function proposed consists of two terms. The first term is to discourage the use of unnecessary connections, and the second term is to prevent the weights of the connections from taking excessively large values. Simple criteria for eliminating weights from the network are also given. The effectiveness of this penalty function is tested on three well-known problems: the contiguity problem, the parity problems, and the monks problems. The resulting pruned networks obtained for many of these problems have fewer connections than previously reported in the literature.

ANALYSIS OF MULTIDIMENSIONAL XOR CLASSIFICATION PROBLEM WITH EVOLUTIONARY FEEDFORWARD NEURAL NETWORKS

2007 ◽  
Vol 16 (01) ◽  
pp. 111-120 ◽  
Author(s):  
MANISH MANGAL ◽  
MANU PRATAP SINGH
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Neural Networks ◽  
Search Algorithm ◽  
Random Search ◽  
Feedforward Neural Networks ◽  
Feedforward Neural Network ◽  
Data Sets ◽  
Classification Problems ◽  
Random Search Algorithm

This paper describes the application of two evolutionary algorithms to the feedforward neural networks used in classification problems. Besides of a simple backpropagation feedforward algorithm, the paper considers the genetic algorithm and random search algorithm. The objective is to analyze the performance of GAs over the simple backpropagation feedforward in terms of accuracy or speed in this problem. The experiments considered a feedforward neural network trained with genetic algorithm/random search algorithm and 39 types of network structures and artificial data sets. In most cases, the evolutionary feedforward neural networks seemed to have better of equal accuracy than the original backpropagation feedforward neural network. We found few differences in the accuracy of the networks solved by applying the EAs, but found ample differences in the execution time. The results suggest that the evolutionary feedforward neural network with random search algorithm might be the best algorithm on the data sets we tested.

DYSLEXIC BEHAVIOUR OF FEEDFORWARD NEURAL NETWORKS

1990 ◽  
Vol 01 (03) ◽  
pp. 237-245 ◽  
Author(s):  
Edgardo A. Ferrán ◽  
Roberto P. J. Perazzo
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Boolean Function ◽  
Cost Function ◽  
Feedforward Neural Networks ◽  
Feedforward Neural Network ◽  
Symmetry Class ◽  
Symmetry Properties ◽  
Partial Learning

A model is proposed in which the synaptic efficacies of a feedforward neural network are adapted with a cost function that vanishes if the boolean function that is represented has the same symmetry properties as the target one. The function chosen according to this procedure is thus taken as an archetype of the whole symmetry class. Several examples are presented showing how this type of partial learning can produce a behaviour of the net that is reminiscent of that of dyslexic persons.

Recurrent Neural Networks for Fault Diagnosis and Severity Assessment of a Screw Compressor

1999 ◽  
Vol 121 (4) ◽  
pp. 724-729 ◽  
Author(s):  
C. James Li ◽  
Yimin Fan
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Recurrent Neural Network ◽  
Recurrent Neural Networks ◽  
Experimental Validation ◽  
Feedforward Neural Networks ◽  
Previous Method ◽  
Feedforward Neural Network ◽  
Screw Compressor ◽  
Severity Assessment

This paper describes a method to diagnose the most frequent faults of a screw compressor and assess magnitude of these faults by tracking changes in compressor’s dynamics. To determine the condition of the compressor, a feedforward neural network model is first employed to identify the dynamics of the compressor. A recurrent neural network is then used to classify the model into one of the three conditions including baseline, gaterotor wear and excessive friction. Finally, another recurrent neural network estimates the magnitude of a fault from the model. The method’s ability to generalize was evaluated. Experimental validation of the method was also performed. The results show significant improvement over the previous method which used only feedforward neural networks.

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