An Optimized Deep-Learning-Based Low Power Approximate Multiplier Design

作     者：M.Usharani B.Sakthivel S.Gayathri Priya T.Nagalakshmi J.Shirisha 

作者机构：Department of Electronics and Communication EngineeringEr.Perunal Manimekalai College of EngineeringKonneripalliHosur635117India Department of Electronics and Communication EngineeringPandian Saraswathi Yadav Engineering CollegeSivagangaiTamilnadu630561India Department of Electronics and Communication EngineeringR.M.D Engineering CollegeGummidipundiTamilnadu601206India Department of Computer Science and EngineeringKoneru Lakshmaiah Education FoundationVaddeswaramGuntur522502India Department of Electronics and Communication EngineeringMalla Reddy Engineering CollegeHyderabadTelangana500015India 

出 版 物：《Computer Systems Science & Engineering》 (计算机系统科学与工程（英文）)

年 卷 期：2023年第44卷第2期

页      面：1647-1657页

核心收录：

学科分类：07[理学] 0701[理学-数学] 070101[理学-基础数学] 

主　　题：Deep learning approximate multiplier LSTM jellyfish 

摘      要：Approximate computing is a popularfield for low power consumption that is used in several applications like image processing,video processing,multi-media and data *** Approximate computing is majorly performed with an arithmetic circuit particular with a *** multiplier is the most essen-tial element used for approximate computing where the power consumption is majorly based on its *** are several researchers are worked on the approximate multiplier for power reduction for a few decades,but the design of low power approximate multiplier is not so *** seems a bigger challenge for digital industries to design an approximate multiplier with low power and minimum error rate with higher *** overcome these issues,the digital circuits are applied to the Deep Learning(DL)approaches for higher *** recent times,DL is the method that is used for higher learning and prediction accuracy in severalfi***,the Long Short-Term Memory(LSTM)is a popular time series DL method is used in this work for approximate *** provide an optimal solution,the LSTM is combined with a meta-heuristics Jel-lyfish search optimisation technique to design an input aware deep learning-based approximate multiplier(DLAM).In this work,the jelly optimised LSTM model is used to enhance the error metrics performance of the Approximate *** optimal hyperparameters of the LSTM model are identified by jelly search ***fine-tuning is used to obtain an optimal solution to perform an LSTM with higher *** proposed pre-trained LSTM model is used to generate approximate design libraries for the different truncation levels as a func-tion of area,delay,power and error *** experimental results on an 8-bit multiplier with an image processing application shows that the proposed approx-imate computing multiplier achieved a superior area and power reduction with very good results on error rates.