Background: In this paper, we present a theoretical discussion on neuromorphic computing
circuit dynamic and its relations with AI deep learning neural networks. The hardware implememtations
of neuromorphic computing and AI deep learning neuronal networks are discussed.
Objective: The investigation is motivated by the design of a feasible fast and energy-efficient circuit
device as well as an efficient training computation method to solve complex classification problems
using AI neuronal networks.
Methods: We focus on the investigations of solving pattern classification and recognition problems in
real applications from the perspectives of both logic computation view and physical circuit device
views. FPGA approaches are considered and a mapping from logic level to physical level is proposed.
Results: A pragmatic mapping method is derived. FPGA method is proposed.
Conclusion: Thus we propose in this paper an approach to solve complex classification problems.
First, the neuromorphic computing as a new research area is introduced, including physical circuit
properties, memristive device physical properties and the circuit dynamics described by the temporal
and spatial (Maxwell) differential equations. Secondly, we show that by using AI deep learning
neural networks to train AI neural networks we are able to derive the optimal AI neuron network
weights. Last but not the least, we brief a mapping method and show in general how the neuromorphic
circuit will work in practice after mapping the weights from AI deep learning neural networks into the
neuromorphic circuit synapses/memristors. We also devote our discussions to the physical device feasibility
and related matters. The method proposed in this paper is pragmatic and constructive.