Artificial olfaction system with hardware on-chip learning neural networks

-The paper presents a method for FPGA implementation of Self-Organizing Map (SOM) artificial neural networks with on-chip learning algorithm. The method aims to build up a specific neural network using generic blocks designed in the MathWorks Simulink environment. The main characteristics of this original solution are: on-chip learning algorithm implementation, high reconfiguration capability and operation under real time constraints. An extended analysis has been carried out on the hardware resources used to implement the whole SOM network, as well as each individual component block.

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“…A sample case study for an implemented design is presented in [22], [23], based on artificial olfaction systems.…”

Section: Discussionmentioning

confidence: 99%

SOM neural network design – A new Simulink library based approach targeting FPGA implementation

Tisan

Cirstea

2013

Mathematics and Computers in Simulation

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“…Owing to the use of general purpose data path elements, the area trade-off in this implementation is not suitable for networks that have multiple hidden layers and larger number of neurons per layer. The authors in [10] present a hardware implementation of multilayer FFNN using existing system generator library functions that are mapped on to the FPGA during synthesis. In this implementation, code from Simulink blocks has been mapped on to corresponding FPGA resources without structural optimization.…”

Section: Previous Workmentioning

confidence: 99%

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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“…Neural modules, which can emulate in hardware the FF-BP computing functions, are grouped into a neural library and can in principle be used to create any FF-BP NN topology by setting the NN characteristics as number of neurons and layers. The case study shows an ANN used as a pattern recognition module in an artificial olfaction system, which is capable to identify four coffee brands [107]. An extended analysis has been carried out regarding the recognition rates versus training data features and data representation.…”

Section: B Case Study 2: Fpga Nn Based Electronic Nosementioning

confidence: 99%

FPGAs in Industrial Control Applications

Monmasson

Idkhajine

Cirstea

et al. 2011

IEEE Trans. Ind. Inf.

460

182

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-The aim of this paper is to review the state of the art of Field Programmable Gate Array (FPGA) technologies and their contribution in industrial control applications. Authors start by addressing various research fields where FPGAs are useful. The features of these devices are then presented followed by their corresponding design tools. To illustrate the benefit of using FPGAs in the case of complex control applications, a sensorless motor controller has been treated. This controller is based on the Extended Kalman Filter. Its development has been made according to a dedicated design methodology which is also discussed. Another example of illustration is the Neural Network Systems. To show the interest of FPGAs in this field, some case studies have been presented.

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Artificial olfaction system with hardware on-chip learning neural networks

Cited by 12 publications

References 12 publications

SOM neural network design – A new Simulink library based approach targeting FPGA implementation

SOM neural network design – A new Simulink library based approach targeting FPGA implementation

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

FPGAs in Industrial Control Applications

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