Optimized Image Multiplication with Approximate Counter Based Compressor

作     者：M.Maria Dominic Savio T.Deepa1,N.Bharathiraja Anudeep Bonasu 

作者机构：SRMISTChennai603203India Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering CollegeChennai601206India Intel CorportationCalifornia93657USA 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2022年第72卷第8期

页      面：3815-3834页

核心收录：

学科分类：0831[工学-生物医学工程（可授工学、理学、医学学位）] 0808[工学-电气工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 07[理学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 0801[工学-力学（可授工学、理学学位）] 070101[理学-基础数学] 

主　　题：Multiplier PSNR image processing approximate compressor NED 

摘      要：The processor is greatly hampered by the large dataset of picture or multimedia *** logic of approximation hardware is moving in the direction of multimedia processing with a given amount of acceptable *** study proposes various higher-order approximate counter-based compressor(CBC)using input shuffled 6:3 *** the Wallace multiplier using a CBC is a significant factor in partial product *** the design of 10-4,11-4,12-4,13-4 and 14-4 CBC are proposed in this paper using an input shuffled 6:3 compressor to attain two stage *** input shuffling aims to reduce the output combination of the 6:3 compressor from 64 to *** of 15-4,10-4,9-4,and 7-3 CBCs are performed using the proposed 6:3 compressor and the results obtained are compared with the existing *** existing models are constructed using multiplexers and 5-3 *** compared to input shuffled 5-3 the proposed 6:3 compressor shows better results in terms of area,power and *** approximation is performed on the 6:3 compressor to further reduce the computational energy of the system which is optimal for multimedia *** major contribution of this work is the development of two stage multiplier using various proposed *** designs of the approximate compressor(AC)and true compressor(TC)are analysed with 8 ×8 and 16 × 16 *** proposed multipliers also provide adequate levels of accuracy,according to the MATLAB simulations,in addition to greater hardware *** the result approximate circuits over image processing shows the stunning performance in many deep learning network in the current research which is only oriented to multimedia.