Analysis of neural algorithms for parallel architectures
E. Di Zitti, Giovanni Maria Bisio, Daniele D. Caviglia, Marco Chirico, G. Parodi · 2003
The computational properties (global and local convergence, computational complexity and algorithm tuning) of neural algorithms are analyzed from a circuit perspective. Different equivalent algorithms are derived by applying various numerical techniques (multistep and relaxation methods) to the stationary and dynamic analysis of a common neural circuit model. Simulation experiments are presented for an 8-bit analog-to-digital-converter implementation of a Hopfield neural network. The implications for the simulation of these neural algorithms on parallel architectures are pointed out.>