TA1b.1

A Hardware-Oriented QAM Demodulation Method Driven by AW-SOM Machine Learning

Lorenzo Canese, Gian Carlo Cardarilli, Luca Di Nunzio, Rocco Fazzolari, Marco Re, Sergio Spanò, University of Rome, Italy
Session:
TA1b: Architectures for Machine Learning Lecture
Track:
Architectures and Implementation
Location:
Evergreen
Presentation Time:
Tue, 31 Oct, 10:15 - 10:40 PT (UTC -8)
Session Chair:
Milos Ercegovac, University of California, Los Angeles
Presentation
Discussion
Resources
No resources available.
Session TA1b
TA1b.1: A Hardware-Oriented QAM Demodulation Method Driven by AW-SOM Machine Learning
Lorenzo Canese, Gian Carlo Cardarilli, Luca Di Nunzio, Rocco Fazzolari, Marco Re, Sergio Spanò, University of Rome, Italy
TA1b.2: Synaptic Turnover Promotes Efficient Learning in Bio-Realistic Spiking Neural Networks
Nikos Malakasis, Spyridon Chavlis, Panayiota Poirazi, Foundation for Research and Technology-Hellas (FORTH), Greece
TA1b.3: An Efficient Dot-Product Unit Based on Online Arithmetic for Variable Precision Applications
Saeid Gorgin, MohammadH. Golamrezaei, Jeong-A Lee, Chosun University, Republic of Korea; Miloˇs D. Ercegovac, University of California, Los Angeles, United States
TA1b.4: MSDF-SVM: Advantage of Most Significant Digit First Arithmetic for SVM Realization
Saeid Gorgin, Mohammadreza Najafi, Mohammad H. Golamrezaei, Jeong-A Lee, Chosun University, Republic of Korea; Miloˇs D. Ercegovac, University of California, Los Angeles, United States
Contacts