Brain Inspired Computing Models and Architectures

With an exponential increase in the amount of data collected per day, the fields of artificial intelligence and machinelearning continue to progress at a rapid pace with respect to algorithms, models, applications, and hardware. In particular,deep neural networks have revolutionized these fields by providing unprecedented human-like performance in solving manyreal-world problems such as image or speech recognition. There is also significant research aimed at unraveling the principles of computation in large biological neural networks and, in particular, biologically plausible spiking neural networks. This paperpresents an overview of the brain-inspired computing models starting with the development of the perceptron and multi-layerperceptron followed by convolutional neural networks (CNNs) and recurrent neural networks (RNNs). The paper brieflyreviews other neural network models such as Hopfield neural networks and Boltzmann machines. Other models such as spikingneural networks (SNNs) and hyperdimensional computing are then briefly reviewed. Recent advances in these neural networksand graph related neural networks are then described.Index Terms—Brain-inspired computing, spiking neural networks, neuromorphic architecture, graph neural networks, hyperdimensional computing, low-power inference, energy efficiency, surrogate gradients.