@misc{BogunPerezBoschQuesadaPerezetal., author = {Bogun, Nicolas and Perez-Bosch Quesada, Emilio and P{\´e}rez, Eduardo and Wenger, Christian and Kloes, Alexander and Schwarz, Mike}, title = {Analytical Calculation of Inference in Memristor-based Stochastic Artificial Neural Networks}, series = {29th International Conference on Mixed Design of Integrated Circuits and System (MIXDES), 23-24 June 2022 , Wrocław, Poland}, journal = {29th International Conference on Mixed Design of Integrated Circuits and System (MIXDES), 23-24 June 2022 , Wrocław, Poland}, isbn = {978-83-63578-22-0}, doi = {10.23919/MIXDES55591.2022.9838321}, pages = {83 -- 88}, abstract = {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.}, language = {en} }