Optimizing FPGA implementation of Feed-Forward Neural Networks

Oniga, Stefan; Tisan, Alin; Mic, Daniel; Buchman, Attila; Vida-Ratiu, A.

doi:10.1109/optim.2008.4602494

Cited by 12 publications

(6 citation statements)

References 4 publications

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“…It should be noted that the effect of the number of bits in the representation is chosen discreetly or arbitrarily by authors [11,19,20,60,61,66,67,69,70,71,75,79,80,81], usually based on the experience or previous works. At most, a scan is performed for a discrete set of bit numbers, without performing a systematic study of the effect of the number of bits on the functionality.…”

Section: State Of the Art Of Hardware Implementation For Sigmoidal Fumentioning

confidence: 99%

Fixed-point design methodology of the hyperbolic tangent function using lookup tables with performance evaluation on FPGA

Suárez¹

2018

EasyChair Preprints

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The sigmoid functions, including the hyperbolic tangent, are widely used in artificial neural networks. Usually, artificial neural networks are implemented on a computer, in training and testing phases, using floating point arithmetic. In some situations it is necessary to improve the performance of neural networks, in such cases the implementations are designed for specific digital devices, using fixed point arithmetic. This strategy increases the speed, and reduces the hardware resources and consumed power. The sigmoid functions are non-linear systems because include exponentials and divisions operations, so they are the bottleneck of the artificial neuron design and cannot be implemented directly in fixed-point format. For this reason, several methods of approximation are used, mainly based on piecewise approximations. One of the most common technique is to use Look-up Tables, which store samples of the function. This method needs a lot of hardware resources, but it reaches the highest speed. The first objective, using lookup table approximations, is to explore the set of solutions and study the effect of the number of bits. This can only be achieved if an advanced design methodology is used for experimentation, a second objective is to expose this fast and flexible design method. Thirdly, the measure of functionality associated with the error is reviewed. A new functionality measure is proposed associated with the concept of signal to noise power relation. The new measure allows compare different approximations; besides, some linearities are observed against the number of bits. Afterwards, the implementations are developed on some Field Programmable Gate Arrays and the performances are evaluated: hardware resources, speed and consumed power. Again, some linear behaviours are observed. Finally, a quality factor is proposed, which includes functionality and physical performances, this factor allows comparison between different implementations.

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Section: State Of the Art Of Hardware Implementation For Sigmoidal Fumentioning

confidence: 99%

Fixed-point design methodology of the hyperbolic tangent function using lookup tables with performance evaluation on FPGA

Suárez¹

2018

EasyChair Preprints

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“…Finding a neural network model with good performance for a given application which is also easy to implement in hardware is not exactly an easy task. Only after several simulations of different ANN models we have opted for a Feed-Forward Backpropagation (FF-BP) ANN that give good results and also is relatively easy to implement in hardware using microcontrollers or FPGAs [10]- [13]. We have made many simulations in order to find the optimal number of hidden layers and number of neurons per hidden layer(s).…”

Section: Human Activity and Health Status Recognitionmentioning

confidence: 99%

Activity Recognition in Adaptive Assistive Systems Using Artificial Neural Networks

Oniga¹,

Sütő²

2016

ElAEE

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1 Abstract-Our research was oriented to develop technologies for independent daily life assistance of elderly or sick persons and to improve the quality of human life. We designed a complex assistive system that can learn and adapt due to the uses of artificial neural networks (ANN). This paper presents the system developed for human activity and health parameters monitoring (temperature, heart rate, acceleration) and focuses on studies and results obtained on arm posture recognition, body posture recognition and usual activities recognition like: lying on various sides, sitting, standing, walking, running, descending or climbing stairs etc. For pattern recognition from the possible biologically inspired algorithms we opted for the ANNs. One direction of research was the design and test of several Matlab ANN models in order to find the best performing architecture. Another research direction was related to the necessary preprocessing of raw data aiming to have a better recognition rate. We find that standard deviation could be used with very good results as a supplementary input data for neurons. We optimized the number of sensors and their placement in order to obtain the best trade-off between recognition rate and the complexity of the recognition system.

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“…Through various optimizations, presented in [6], the resources can be decreased to 10 slices for a MAC implemented with a dedicated multiplier in VIRTEX-II.…”

Section: A Implementation Of the Multiply And Accumulate Blockmentioning

confidence: 99%

FPGA implementation of Feed-Forward Neural Networks for smart devices development

Oniga¹,

Tisan²,

Mic³

et al. 2009

2009 International Symposium on Signals, Circuits and Systems

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This paper presents the results obtained in the implementation of Feed-Forward Artificial Neural Networks (FF-ANN) with one or several layers, used in the development of smart devices that needs learning capability and adaptive behavior. The networks were implemented using ANN specific blocks created by the authors using the System Generator software. The training and the testing of the networks was conducted using sets of 150 training and test vectors with 7 elements.

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Optimizing FPGA implementation of Feed-Forward Neural Networks

Cited by 12 publications

References 4 publications

Fixed-point design methodology of the hyperbolic tangent function using lookup tables with performance evaluation on FPGA

Fixed-point design methodology of the hyperbolic tangent function using lookup tables with performance evaluation on FPGA

Activity Recognition in Adaptive Assistive Systems Using Artificial Neural Networks

FPGA implementation of Feed-Forward Neural Networks for smart devices development

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