An Artificial Neural Network Architecture Based on Reversible Logic

We can investigate and utilize complex 2.5D/3D SOC design architectures thanks to high-performance computing that goes beyond sub-10 nm advanced node technology. Although complex design, heterogeneous integration, and node scaling allow us to think beyond Moore's law, they also restrict the scope due to worries about excessive power dissipation. Reversible logic functions and quantum computation have been studied recently in relation to nanotechnology and low power VLSI circuit designs. Digital circuits with reversible computation show much lower power dissipation. In this paper, we use reversible logic gates to propose a novel Artificial Neural Network (ANN) design. Only a few related articles were found after a thorough search of the pertinent literature. To the best of our knowledge, our suggested method is the first to use only reversible logic gates to implement a full feedforward neural network circuit. When compared to current methods, the comparative analysis shows that our suggested strategy has reduced overall power dissipation by about 16%. Additionally, the suggested method is more scalable than the traditional design method.