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An Optimized Deep-Learning-Based Low Power approximate multiplier Design

Computer Systems Science & Engineering 2023年第2期44卷 1647-1657页

作者： M.Usharani B.Sakthivel S.Gayathri Priya T.Nagalakshmi J.Shirisha Department of Electronics and Communication Engineering Er.Perunal Manimekalai College of EngineeringKonneripalliHosur635117India Department of Electronics and Communication Engineering Pandian Saraswathi Yadav Engineering CollegeSivagangaiTamilnadu630561India Department of Electronics and Communication Engineering R.M.D Engineering CollegeGummidipundiTamilnadu601206India Department of Computer Science and Engineering Koneru Lakshmaiah Education FoundationVaddeswaramGuntur522502India Department of Electronics and Communication Engineering Malla Reddy Engineering CollegeHyderabadTelangana500015India

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.

关键词： Deep learning approximate multiplier LSTM jellyﬁsh

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