Title :
SET based Boltzmann machine and Hopfield neural networks
Author :
Liu, Chia-Chin ; Chen, Chunhong
Author_Institution :
Dept. of Electr. & Comput. Eng., Univ. of Windsor, Windsor, ON, Canada
Abstract :
This paper presents a method of implementing Boltzmann machine and Hopfield neural networks using single electron devices. Comparison between these two networks is shown by computer simulations in terms of their ability to converge to a global minimum state. It is demonstrated that the probabilistic nature of single electron tunneling phenomena enables the stochastic neuron operation with Boltzmann machine.
Keywords :
Boltzmann machines; Hopfield neural nets; single electron devices; stochastic processes; Boltzmann machine; Hopfield neural networks; SET; computer simulations; single electron devices; single electron tunneling; stochastic neuron operation; Biological neural networks; Capacitance; Energy states; Inverters; Neurons; Simulation; Stochastic processes; Boltzmann machine; Hopfield neural network; global minimum energy state; single-electron tunnelling;
Conference_Titel :
Nanotechnology (IEEE-NANO), 2011 11th IEEE Conference on
Conference_Location :
Portland, OR
Print_ISBN :
978-1-4577-1514-3
Electronic_ISBN :
1944-9399
DOI :
10.1109/NANO.2011.6144315
