Design of encoder and decoder using reversible logic gates

Abstract

The digital modules are the building block of all the applications in digital world. Encoders and decoders play a vital role in numerous applications. The Encoder - Decoder framework is a major stream deep learning design for all computing applications. The "encoder" uses a deep convolutional network to encode hierarchical data into feature maps, whereas the "decoder" uses encoded features to create efficient dense forecasts. The Encoder - Decoder platform's core principle is to understand data first and then forecast segmentation depending on the interpretation of the data. Reversible gates are the blooming technique now a day as they are really useful in reducing the power consumption.When the components are implemented using these gates it gives the optimized result. By analyzing the working of encoders and decoders, 8x3 encoder and decoder, hamming encoder, decoder are proposed using reversible gates. Their functionality is verified and power is obtained using Xilinx and Cadence tools. The proposed method of encoder and decoder show improvement, when compared with the existing designs.