Hardware implementation of Radial Basis Function Neural Network based on sigma-delta modulation

Xiaodan, Guo; Meng, Qiao

doi:10.1109/csndsp.2014.6923984

Cited by 4 publications

(2 citation statements)

References 12 publications

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“…Such function is used, for example, in the realizations of the Radial Basis Function Neural Networks (RBF-NNs). NNs of this type are mainly realized using the FPGA platforms [42][43][44][45]. Such realizations, despite some advantages (time and cost) suffer from several limitations.…”

Section: Neighborhood Functionmentioning

confidence: 99%

An Energy-Efficient, Parallel Neighborhood and Adaptation Functions for Hardware Implemented Self-Organizing Maps Applied in Smart Grid

Kolasa

2020

Energies

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Smart Grids (SGs) can be successfully supported by Wireless Sensor Networks (WSNs), especially through these consisting of intelligent sensors, which are able to efficiently process the still growing amount of data. We propose a contribution to the development of such intelligent sensors, which in an advanced version can be equipped with embedded low-power artificial neural networks (ANNs), supporting the analysis and the classification of collected data. This approach allows to reduce the energy consumed by particular sensors during the communication with other nodes of a larger WSN. This in turn, facilitates the maintenance of a net of such sensors, which is a paramount feature in case of their application in SG devices distributed over a large area. In this work, we focus on a novel, energy-efficient neighborhood mechanism (NM) with the neighborhood function (NF). This mechanism belongs to main components of self learning ANNs. We propose a realization of this component as a specialized chip in the CMOS technology and its optimization in terms of the circuit complexity and the consumed energy. The circuit was realized as a prototype chip in the CMOS 130 nm technology, and verified by means of transistor level simulations and measurements.

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Section: Neighborhood Functionmentioning

confidence: 99%

An Energy-Efficient, Parallel Neighborhood and Adaptation Functions for Hardware Implemented Self-Organizing Maps Applied in Smart Grid

Kolasa

2020

Energies

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“…Various SDM-based DSP applications for word length reduction and hence reducing the overall system complexity are reflected in the literature that includes but is not limited to simple arithmetic units [9][10][11], FIR filters (fully or partially transformed to single-bit) [12][13][14][15][16][17][18][19], IIR filters [20][21][22], and some complex adaptive filter structures [23][24][25][26]. Other recent examples of SDM-based short word length systems include adaptive channel equalizers using a µ-less approach [27], Weiner filters [28], Matched filters [29], digital arithmetic units [30], smart sensor communications [31], correlation-less filters [32,33] and latest SDM-Based Image Processing [34].…”

Section: Introductionmentioning

confidence: 99%

A novel approach toward optimized image processing using sigma delta modulation

Pathan,

Memon,

Aziz

et al. 2024

Mehran Univ. res. j. eng. technol.

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Image processing has widespread uses practically in every branch of science and arts. Processing images is more difficult than processing sound or data as there are more bits in the high pixel quality image. It requires more space to store the image, more bandwidth to transmit it, and more time and resources to process. An image's complexity may decrease if its bit size is decreased. Sigma-delta modulation, or SDM for short, is an alternative method of minimizing data-word length to compression. Digital signal processing (DSP) systems can be made simpler by using the SDM approach, which was first created for analog to digital conversion (ADC). This paper suggests a novel way to use SDM in MATLAB for improved image processing. Consequently, the suggested single-bit SDM-based image arithmetic architecture is tested and compared with the traditional image arithmetic techniques. Additionally, to see the noisy channel influence on the traditional and proposed systems, some statistical metrics are also studied at different noise variance values, such as signal to noise ratio (SNR), mean square error (MSE), and Peak SNR value. The suggested architecture for the SDM-based image arithmetic precisely matches the addition and subtraction results of the conventional design, even yielding a higher SNR and the same Peak SNR as the traditional methods. In contrast, the outcomes of division and multiplication fall within an acceptable range. For better results the over-sampling ratio (OSR), an inherent characteristic of SDM must be increased at the cost of more processing cycles. Therefore, the trade-off between fewer resources, limited transmission bandwidth, and comparatively more cycles is provided by the SDM-based technique.

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Computationally Efficient Low-Power Sigma-Delta Modulation-Based Image Noise Removal Filter

Pathan,

Memon,

Shah

et al. 2024

Circuits Syst Signal Process

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Hardware implementation of Radial Basis Function Neural Network based on sigma-delta modulation

Cited by 4 publications

References 12 publications

An Energy-Efficient, Parallel Neighborhood and Adaptation Functions for Hardware Implemented Self-Organizing Maps Applied in Smart Grid

An Energy-Efficient, Parallel Neighborhood and Adaptation Functions for Hardware Implemented Self-Organizing Maps Applied in Smart Grid

A novel approach toward optimized image processing using sigma delta modulation

Computationally Efficient Low-Power Sigma-Delta Modulation-Based Image Noise Removal Filter

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