Short- and long-term dynamics in a stochastic pulse stream neuron implemented in FPGA

Rossmann, M.; Bühlmeier, Andreas; Manteuffel, Gerhard; Goser, K.

doi:10.1007/bfb0020321

Cited by 6 publications

(2 citation statements)

References 5 publications

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“…The test bench waveform is a graphical view of a test bench. Figure (6) shows the simulation results of the heart condition input.…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, literature survey for many previously published researches is presented as follows: S. Barro, et al, 1998 [5] described the application of a new artificial neural network model aimed at the morphological classification of heartbeats detected on ECG signal. Rui Mendes, et al, 2002 [6] carried out about Particle Swarm for Feed forward Neural Networks and make comparison between PSO and other approaches to show the benefits of PSO for training FNN when applied to classification and regression tasks. This work was only meant as a starting point in this field and the results were encouraging, considering that PSO is a general purpose method, where no specific knowledge is used.…”

Section: Related Workmentioning

confidence: 99%

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ECG Signal Diagnoses Using Intelligent Systems Based on FPGA

M. Abdul Kareem,

A.R. Akkar

2013

ETJ

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This paper presents the use of Particle Swarm Optimization (PSO), neural networks with the most promising supervised learning algorithms for automatic detection of cardiac arrhythmias based on analysis of the Electrocardiogram (ECG). Artificial Neural Network (ANN) has three layers with ten nodes in the input layer, five nodes in the hidden layer and five nodes in the output layer, which is trained using the PSO algorithm. The trained network was able to classify the ECG signal in normal signal, atrial flutter, ventricular tachycardia, sever conducting tissue and wandering a trial pacemaker. Field Programmable Gate Arrays (FPGAs) have been used to implement ANN trained by the supervised learning algorithms and PSO, because of their speed benefits, as well as the re-programmability of the FPGAs which can support the reconfiguration necessary to program a neural network. A VHDL Design of ANN platform is proposed to evolve the architecture ANN circuits using FPGA-Spartan 6 Evaluation board. The VHDL design platform creates ANN design files using WebPACKTM ISE 13.3 program. All the algorithms used to train the ANN showed high effectiveness with 100% classification.

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“…The test bench waveform is a graphical view of a test bench. Figure (6) shows the simulation results of the heart condition input.…”

Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

ECG Signal Diagnoses Using Intelligent Systems Based on FPGA

M. Abdul Kareem,

A.R. Akkar

2013

ETJ

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FPGA Implementations of Neural Networks

Omondi¹,

Rajapakse²

2006

200

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In this article, I would like to discuss the implementation and using of neural networks on an FPGA [1] based system and also the topology of neural network deployment on FPGA, as well as the advantages of using FPGA-based neural networks over a CPU-based neural network. We will also discuss the requirements for neural networks deployed in an embedded system. Concluding with the advantages and disadvantages of the neural networks produced by the FPGA manufacturers as well as the advantages of using HLS[2] (High Level Synthesis) technology. And why SoC together with FPGA makes an ideal technological solution for embedded systems. We will also discuss which high-level programming languages will make it easier to develop neural networks on FPGA. АННОТАЦИЯ В этой статье я хотел бы обсудить реализацию и использование нейронных сетей на базе ПЛИС, топологию нейронных сетей на базе ПЛИС, а также преимущества использования нейронных сетей на базе ПЛИС перед нейронными сетями на основе обычных процессоров. Мы также обсудим требования к нейронным сетям, развернутым на встраиваемых системах. В заключение мы рассмотрим преимущества и недостатки нейронных сетей, выпускаемых производителями ПЛИС, а также преимущества использования технологии HLS (High Level Synthesis). Почему SoC вместе с ПЛИС являются идеальным технологическим решением для встраиваемых систем, а также обсудим, какие языки программирования высокого уровня облегчат разработку нейронных сетей на ПЛИС.

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FPNA: Concepts and Properties

Girau

FPGA Implementations of Neural Networks

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Short- and long-term dynamics in a stochastic pulse stream neuron implemented in FPGA

Cited by 6 publications

References 5 publications

ECG Signal Diagnoses Using Intelligent Systems Based on FPGA

ECG Signal Diagnoses Using Intelligent Systems Based on FPGA

FPGA Implementations of Neural Networks

FPNA: Concepts and Properties

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