Effectiveness of Recurrent Neural Network with Biomimic Components

In order to improve the energy efficiency and computational capacity of our data processing systems, bioinspired, or neuromorphic, computing draws inspiration from how the brain computes. Emulation of natural systems, patterns, and components is known as biomimetics or biomimicry, and it is done to address challenging challenges in society. The term “spintronics” refers to the application of a fundamental property of particles (the spin of an electron) known as the spin to information processing. Hopfield was the first to establish a direct connection between physical models and neural networks. To learn any of these models, the neural network must be able to adjust the coupling between each pair of spins. To implement multi-layer perceptron (neural network) structures, current technologies, however, have necessitated the extensive usage of complementary metal-oxide-semiconductor (CMOS) devices, which raises the perceptron's complexity of manufacture, space need, and power consumption. In this paper, it is suggested that the first trainable CMOS-free recurrent neural network with biomimetic components can be built using biomimetic neurons made of domain wall-magnetic tunnel junctions. This analysis shows how well this system performs computationally on benchmark tasks and how much more efficiently it performs computationally than alternative recurrent neural network systems.