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Title: | Power and Area Efficient Intelligent Hardware Design for Water Quality Applications |
Authors: | Gupta, Anu Gupta, Rajiv |
Keywords: | EEE Artificial Neural Network (ANN) Activation function ASIC Power efficient Water Quality Computational complexity |
Issue Date: | Nov-2018 |
Publisher: | International Frequency Sensor Association |
Abstract: | The paper presents a power efficient and computationally less intensive intelligent hardware using artificial neural network for water quality applications. A compact Hardware Neural Network algorithm has been developed that takes four water quality parameters as the input vector and perform classification of the parameters using a Multilayer Perceptron Network. The computational complexity in the implementation of logistic function has been reduced at a mathematical level by use of approximation methods such as Pad===?=== approximation for exponential function and non- linear approximation for sigmoid function. The network improves accuracy of the output by learning by back-propagation of the error. Results show that non-linear approximation method is 34.13 % power efficient and utilizes 15.53 % less number of hardware resources in comparison to Pad===?===. ASIC implementation is compact and has 99 % less power consumption as compared to FPGA implementation of the same algorithm. |
URI: | https://www.sensorsportal.com/HTML/DIGEST/P_3037.htm http://dspace.bits-pilani.ac.in:8080/xmlui/handle/123456789/9169 |
Appears in Collections: | Department of Electrical and Electronics Engineering |
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