Article Per Year (5 Year)
p-Index From 2021 - 2026
0
P-Index
This Author published in this journals
All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control)
Mario Patetta
University of Rome Tor Vergata
Computer Science & IT

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Approximated computing for low power neural networks Gian Carlo Cardarilli; Luca Di Nunzio; Rocco Fazzolari; Daniele Giardino; Marco Matta; Mario Patetta; Marco Re; Sergio Spanò
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 17, No 3: June 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v17i3.12409

Abstract

This paper investigates about the possibility to reduce power consumption in Neural Network using approximated computing techniques. Authors compare a traditional fixed-point neuron with an approximated neuron composed of approximated multipliers and adder. Experiments show that in the proposed case of study (a wine classifier) the approximated neuron allows to save up to the 43% of the area, a power consumption saving of 35% and an improvement in the maximum clock frequency of 20%.
Co-Authors Daniele Giardino Gian Carlo Cardarilli Luca Di Nunzio Marco Matta Marco Re Rocco Fazzolari Sergio Spanò