  Modern AI systems, based on von Neumann architecture and classical neural
networks, have a number of fundamental limitations in comparison with the
brain. This article discusses such limitations and the ways they can be
mitigated. Next, it presents an overview of currently available neuromorphic AI
projects in which these limitations are overcame by bringing some brain
features into the functioning and organization of computing systems (TrueNorth,
Loihi, Tianjic, SpiNNaker, BrainScaleS, NeuronFlow, DYNAP, Akida). Also, the
article presents the principle of classifying neuromorphic AI systems by the
brain features they use (neural networks, parallelism and asynchrony, impulse
nature of information transfer, local learning, sparsity, analog and in-memory
computing). In addition to new architectural approaches used in neuromorphic
devices based on existing silicon microelectronics technologies, the article
also discusses the prospects of using new memristor element base. Examples of
recent advances in the use of memristors in euromorphic applications are also
given.
