Supervised Machine Learning for Training a Neural Network as 5:2 Compressor
Lavanya Maddisetti1, Ranjan K. Senapati2, Ravindra JVR3

1Lavanya Maddisetti, Assistant Professor in Department of ECE, Vardhaman College of Engineering. KL Deemed to be University, Vijayawada,AndhraPradesh,India
2Ranjan K. Senapati, professor in the Department of Electronics and Communications Engineering, KL Deemed to be University. Vijayawada,AndhraPradesh,India
3Ravindra JVR, Department of Electronics and Communications Engineering, Vardhaman College of Engineering, Shamshabad, Telangana

Manuscript received on 02 July 2019 | Revised Manuscript received on 09 July 2019 | Manuscript published on 30 August 2019 | PP: 2079-2084 | Volume-8 Issue-10, August 2019 | Retrieval Number: J93330881019/2019©BEIESP | DOI: 10.35940/ijitee.J9333.0881019
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Abstract: Machine Learning has achieved substantial development in numerous applications like image processing, pattern recognition, approximate computing etc. This paper interlinks supervised machine learning algorithm and VLSI architectures to train a neural network as exact and approximate 5:2 compressors. Probabilistic pruning type of approximation technique has been employed on the exact 5:2 compressor. This approximation technique on compressors reduces the power consumption with variation in the outputs without affecting the error limit. The simulation of 5:2 compressors and training of neural network using machine learning algorithm has been done using Spectre simulator of Cadence Design Systems at 45nm CMOS technology node and Keras library with TensorFlow background respectively.
Keywords: Accuracy, Classification, Machine Learning, Neural Network.
Scope of the Article: Classification