Efficient VLSI Implementation of Neural Networks With Hyperbolic Tangent Activation Function

Zamanlooy, Babak; Mirhassani, Mitra

doi:10.1109/tvlsi.2012.2232321

Cited by 134 publications

(56 citation statements)

References 17 publications

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“…In [39] the authors propose a comparison between two FPGA architectures which uses floating-point accelerators based on RA-LUT to compute fast AFs. The first solution, refined from the one proposed in [40, 41], implements the NN on a soft processor and computes the AFs through a smartly spaced RA-LUT. The second solution is an arithmetic chain coordinated by a VHDL finite state machine.…”

Section: Activation Functions For Fast Computationmentioning

confidence: 99%

On Training Efficiency and Computational Costs of a Feed Forward Neural Network: A Review

Laudani

Lozito

Fulginei

et al. 2015

Computational Intelligence and Neuroscience

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A comprehensive review on the problem of choosing a suitable activation function for the hidden layer of a feed forward neural network has been widely investigated. Since the nonlinear component of a neural network is the main contributor to the network mapping capabilities, the different choices that may lead to enhanced performances, in terms of training, generalization, or computational costs, are analyzed, both in general-purpose and in embedded computing environments. Finally, a strategy to convert a network configuration between different activation functions without altering the network mapping capabilities will be presented.

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Section: Activation Functions For Fast Computationmentioning

confidence: 99%

On Training Efficiency and Computational Costs of a Feed Forward Neural Network: A Review

Laudani

Lozito

Fulginei

et al. 2015

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

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“…For a neuron present in the hidden layer, the weight correction is governed by (2) in which the local gradient δ j (n) is defined by (4) as given below.…”

Section: Candidate Ffnn For Implementationmentioning

confidence: 99%

“…An efficient approach to approximate the hyperbolic tangent activation function was proposed in [2]. The architecture proposed in this paper is based on piecewise linear [2]. The maximum allowable error is used as the design parameter in this architecture.…”

Section: A Hyperbolic Tangent Activation Functionmentioning

confidence: 99%

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FPGA implementation of a multilayer Artificial Neural Network using System-on-Chip design methodology

Biradar

Chatterjee

Mishra

et al. 2015

2015 International Conference on Cognitive Computing and Information Processing(CCIP)

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Artificial NeuralNetworks (ANNs) find applications in engineering solutions to complex problems. The usefulness of ANNs in real-time embedded applications can be enhanced if feasible architectures for their hardware implementation can be customized to provide an attractive tradeoff between area and performance. In this paper, we present a real-time embedded hardware implementation of a feed-forward neural network which employs backpropagation algorithm for training. Custom modules are designed for the activation functions, the neurons and a finite state machine that co-ordinate activities for training. The proposed ANN is implemented on a Virtex-5 Field Programmable Gate Array. Use of System-on-Chip design methodology enables design reuse while improving the performance metrics.

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“…When implementing this algorithm in an embedded environment, the limited computational capabilities calls for a trade-off among precision, memory footprint, and computational cost. Different studies tested the embedded implementation of NNs to achieve optimal results, either by re-arranging the operations required to compute the linear part of the NN [17] to fully exploit pipelining, or by speeding up the costly non-linear activation function through different numerical approximation [18,19,20,21,22,23,24].…”

Section: Introductionmentioning

confidence: 99%

A Neural Network Embedded System for Real-time Estimation of Muscle Forces

Lozito

Schmid

Conforto

et al. 2015

Procedia Computer Science

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This work documents the progress towards the implementation of an embedded solution for muscular forces assessment during cycling activity. The core of the study is the adaptation to a real-time paradigm an inverse biomechanical model. The model is well suited for real-time applications since all the optimization problems are solved through a direct neural estimator. The real-time version of the model was implemented on an embedded microcontroller platform to profile code performance and precision degradation, using different numerical techniques to balance speed and accuracy in a low computational resources environment

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Efficient VLSI Implementation of Neural Networks With Hyperbolic Tangent Activation Function

Cited by 134 publications

References 17 publications

On Training Efficiency and Computational Costs of a Feed Forward Neural Network: A Review

On Training Efficiency and Computational Costs of a Feed Forward Neural Network: A Review

FPGA implementation of a multilayer Artificial Neural Network using System-on-Chip design methodology

A Neural Network Embedded System for Real-time Estimation of Muscle Forces

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