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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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Reliability Analysis of Symmetrical Columns with Eccentric Loading from Lindley Distribution

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Communications in Mathematics and Statistics 2020年第2期8卷 135-149页

作者： Penti Hari Prasad T.Sumathi Uma Maheswari j.shirisha CVR College of Engineering HyderabadTelangana 501510India Kakatiya University WarangalTelangana 506009India MLRITM HyderabadTelanganaIndia

This paper shows the reliability of the symmetrical columns with eccentric loading about one and two axes due to the maximum intensity stress and minimum intensity *** this paper,a new lifetime distribution is introduced which is obtained by compounding exponential and gamma distributions(named as Lindley distribution).Hazard rates,mean time to failure and estimation of single parameter Lindley distribution by maximum likelihood estimator have been *** is observed that when the load and the area of the cross section increase,failure of the column also increases at two intensity *** is observed from the results that reliability decreases when scale parameter increases.

关键词： Reliability Lindley distribution Hazard rate Mean time to failure Intensity stress Eccentric load

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Synthesis and Evaluation of Quinolino-Benzoxaboroles as Potential Antimicrobial Agents

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International journal of Organic Chemistry 2022年第2期12卷 53-63页

作者： Greeshma P. Kumpati Michael j. Williams Srinidhi Mereddy joseph L. johnson shirisha jonnalagadda Integrated Biosciences Graduate Program University of Minnesota Duluth MN USA Department of Chemistry and Biochemistry University of Minnesota Duluth Duluth MN USA Department of Biology University of Washington Seattle WA USA

Several quinolino-benzoxaborole derivatives have been prepared to start from aminobenzoxaboroles. These derivatives have been evaluated for their anti-cancer activity on human and murine cancer cell lines and based on their relative non-toxicity, these compounds were further evaluated for their antibacterial activity against E. coli, B. subtilis, and M. smegmatis. The synthesized compounds were also evaluated for antifungal activity in C. albicans and C. neoformans.

关键词： Benzoxaboroles Aminobenzoxaboroles Quinolino-Benzoxaboroles Anti-Microbial Agents

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