An Efficient Selection Gate Logic Based Ripple Carry Adder for Deep Learning Networks

Full adders are fundamental components in digital circuit design, responsible for binary addition. Of late in Deep learning network architectures. Conventional full adder implementations often they are limited in terms of their speed, area, and power consumption. This study proposes a novel full adder architecture using multiplexers to overcome these challenges. This paper presents an enhanced VLSI (Very-Large-Scale Integration). A Ripple Carry Adder based multiplexer specially optimized for deep learning applications compared to traditional full Adder designs. Deep learning is a subset of artificial intelligence (AI) that teaches computers to mimic the human brain and process data. The introduction of this optimized RCA is expected to not only speed up the computation process but contributes to the overall efficiency of the deep learning network, making it more efficient in the design of the next Generation AI Processor. The proposed design utilizes a structured approach leveraging multiplexers to minimize delay and improve overall performance. The design is implemented as a ripple carry adder and extended to 64 bits to demonstrate scalability. The static analysis and simulation results up to 64-bit implementation reveal substantial reductions in delay, area, and power, making this design a highly efficient solution for modern high-speed digital systems