NEURAL NETWORKS IN ANALOG HARDWARE — DESIGN AND IMPLEMENTATION ISSUES

2000 ◽  
Vol 10 (01) ◽  
pp. 19-42 ◽  
Author(s):  
SORIN DRAGHICI
Keyword(s):  
Neural Networks ◽  
Power Consumption ◽  
Hardware Design ◽  
Chip Area ◽  
Design And Implementation ◽  
Trade Offs ◽  
Hardware Implementations ◽  
Noise Susceptibility

This paper presents a brief review of some analog hardware implementations of neural networks. Several criteria for the classification of general neural networks implementations are discussed and a taxonomy induced by these criteria is presented. The paper also discusses some characteristics of analog implementations as well as some trade-offs and issues identified in the work reviewed. Parameters such as precision, chip area, power consumption, speed and noise susceptibility are discussed in the context of neural implementations. A unified review of various "VLSI friendly" algorithms is also presented. The paper concludes with some conclusions drawn from the analysis of the implementations presented.

scholarly journals Bioacoustic Classification of Antillean Manatee Vocalization Spectrograms Using Deep Convolutional Neural Networks

2020 ◽  
Vol 10 (9) ◽  
pp. 3286 ◽  
Author(s):  
Fernando Merchan ◽  
Ariel Guerra ◽  
Héctor Poveda ◽  
Héctor M. Guzmán ◽  
Javier E. Sanchez-Galan
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Classification Rate ◽  
Trichechus Manatus ◽  
Hardware Implementations ◽  
Filter Size ◽  
Software And Hardware ◽  
Search Approach

We evaluated the potential of using convolutional neural networks in classifying spectrograms of Antillean manatee (Trichechus manatus manatus) vocalizations. Spectrograms using binary, linear and logarithmic amplitude formats were considered. Two deep convolutional neural networks (DCNN) architectures were tested: linear (fixed filter size) and pyramidal (incremental filter size). Six experiments were devised for testing the accuracy obtained for each spectrogram representation and architecture combination. Results show that binary spectrograms with both linear and pyramidal architectures with dropout provide a classification rate of 94–99% on the training and 92–98% on the testing set, respectively. The pyramidal network presents a shorter training and inference time. Results from the convolutional neural networks (CNN) are substantially better when compared with a signal processing fast Fourier transform (FFT)-based harmonic search approach in terms of accuracy and F1 Score. Taken together, these results prove the validity of using spectrograms and using DCNNs for manatee vocalization classification. These results can be used to improve future software and hardware implementations for the estimation of the manatee population in Panama.

scholarly journals Design & Implementation of Low Power 3-bit Flash ADC in 0.18μm CMOS

2012 ◽  
pp. 89-93
Author(s):  
Pradeep Kumar ◽  
Amit Kolhe
Keyword(s):  
Power Consumption ◽  
Response Time ◽  
Low Power ◽  
Cmos Technology ◽  
Analog To Digital Converter ◽  
Digital Converter ◽  
Chip Area ◽  
Flash Adc ◽  
Analog To Digital ◽  
Design And Implementation

This paper describes the design and implementation of a Low Power 3-bit flash Analog to Digital converter (ADC). It includes 7 comparators and one thermometer to binary encoder. It is implemented in 0.18um CMOS Technology. The presimulation of ADC is done in T-Spice and post layout simulation is done in Microwind3.1. The response time of the comparator equal to 6.82ns and for Flash ADC as 18.77ns.The Simulated result shoes the power consumption in Flash ADC as is 36.273mw .The chip area is for Flash ADC is 1044um2 .

The ROM Design with Half Grouping Compression Method for Chip Area and Power Consumption Reduction

2009 ◽  
Vol E92-C (3) ◽  
pp. 352-355
Author(s):  
Ki-Sang JUNG ◽  
Kang-Jik KIM ◽  
Young-Eun KIM ◽  
Jin-Gyun CHUNG ◽  
Ki-Hyun PYUN ◽  
...  
Keyword(s):  
Power Consumption ◽  
Compression Method ◽  
Chip Area ◽  
Consumption Reduction

CNN-based Classification of Degraded Images

2020 ◽  
Vol 2020 (10) ◽  
pp. 28-1-28-7 ◽  
Author(s):  
Kazuki Endo ◽  
Masayuki Tanaka ◽  
Masatoshi Okutomi
Keyword(s):  
Neural Networks ◽  
Image Restoration ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Alternative Approach ◽  
Degraded Image ◽  
Degraded Images ◽  
Straightforward Approach

Classification of degraded images is very important in practice because images are usually degraded by compression, noise, blurring, etc. Nevertheless, most of the research in image classification only focuses on clean images without any degradation. Some papers have already proposed deep convolutional neural networks composed of an image restoration network and a classification network to classify degraded images. This paper proposes an alternative approach in which we use a degraded image and an additional degradation parameter for classification. The proposed classification network has two inputs which are the degraded image and the degradation parameter. The estimation network of degradation parameters is also incorporated if degradation parameters of degraded images are unknown. The experimental results showed that the proposed method outperforms a straightforward approach where the classification network is trained with degraded images only.

CLASSIFICATION OF SOUNDS OF ASTHMATIC BREATHING USING NEURAL NETWORKS

2018 ◽  
Author(s):  
G. К. Berdibaeva ◽  
◽  
O. N. Bodin ◽  
D. S. Firsov ◽  
◽  
...  
Keyword(s):  
Neural Networks
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