- The impact of artificial intelligence on human life
has increased significantly in recent years. However, as the
complexity of problems rose aswell, increasing system features
for such amount of data computation became troublesome due
to the von Neumann’s computer architecture. Neuromorphic
computing aims to solve this problem by mimicking the parallel
computation of a human brain. For this approach, memristive
devices are used to emulate the synapses of a human brain. Yet,
common simulations of hardware based networks require time
consuming Monte-Carlo simulations to take into account the
stochastic switching of memristive devices. This work presents
an alternative concept making use of the convolution of the
probability distribution functions (PDF) of memristor currents
by its equivalent multiplication in Fourier domain. An artificial
neural network is accordingly implemented to perform the
inference stage with handwritten digits.