Deep fully Connectedness Convolutional Broken Stick Regressive Brent-Kung Adder Enhancement for Efficient VLSI Design

Very large scale Integration (VLSI) was used to create hardware which initiates by the explanation of algorithm as well as employs computer program. In digital systems, multiplier and adder are the fundamental components in design of integrated circuit applications. High speed devices play an essential role in VLSI applications. Adder enhancement is used in miscellaneous application in new VLSI system like multiplier design, ALU and etc. An efficient adder circuit design is used to optimize various metrics. But, design complexity was not reduced by existing VLSI design. To handle such limitations, Deep Fully Connectedness Convolutional Broken Stick Regressive Brent-Kung Adder Enhancement (DFCCBSRBKAE) Technique is introduced in VLSI circuits. Input layer, two hidden layers as well as output layer comprises four layer of DFCCBSRBKAE Technique. The carry inputs are transferred to input layer. Input data is transferred to hidden layer 1. Broken-stick regression analysis is performed for pre-processing data. Next, pre-processed data is transferred to hidden layer 2. Carry generation and post-processing is carried out in hidden layer 2 and the output results are covered by the convolution. At last, outcomes were attained within output layer. This in turn helps to perform an efficient adder enhancement of digital multiplier with minimal power and time consumption. The performance of DFCCBSRBKAE Technique is determined by power and time consumption. DFCCBSRBKAE Technique reduces the area, speed, and delay and power consumption of adder enhancement compared with conventional methods.