A Reversible-Logic based Architecture for Artificial Neural Network

High-performance computing beyond sub-10 nm advanced node technology allows us to explore and use complex 2.5D/3D SOC design architecture. Node scaling, heterogeneous integration, and complex design enable us to think beyond Moore's law but, at the same time, limit the scope with concerns of excessive power dissipation. The field of quantum computation and reversible logic functions has been researched in recent years in the context of low power VLSI circuit designs and nanotechnology. Reversible computation exhibits significantly reduced power dissipation in digital circuits. In this paper, we propose a novel design of Artificial Neural Network (ANN) using reversible logic gates. A thorough search of the relevant literature yielded only a few related articles. To the best of our knowledge, our proposed approach is the first attempt to implement a complete feedforward neural network circuit using only reversible logic gates. The comparative analysis demonstrates that our proposed approach has achieved an approximately 16% reduction in overall power dissipation compared to existing approaches. The proposed approach also has better scalability than the classical design approach.