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  • Open Access


    Truncation and Rounding-Based Scalable Approximate Multiplier Design for Computer Imaging Applications

    S. Rooban1,*, A. Yamini Naga Ratnam1, M. V. S. Ramprasad2, N. Subbulakshmi3, R. Uma Mageswari4

    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 5169-5184, 2022, DOI:10.32604/cmc.2022.027974

    Abstract Advanced technology used for arithmetic computing application, comprises greater number of approximate multipliers and approximate adders. Truncation and Rounding-based Scalable Approximate Multiplier (TRSAM) distinguish a variety of modes based on height (h) and truncation (t) as TRSAM (h, t) in the architecture. This TRSAM operation produces higher absolute error in Least Significant Bit (LSB) data shift unit. A new scalable approximate multiplier approach that uses truncation and rounding TRSAM (3, 7) is proposed to increase the multiplier accuracy. With the help of foremost one bit architecture, the proposed scalable approximate multiplier approach reduces the partial products. The proposed approximate TRSAM… More >

  • Open Access


    Field Programmable Gate Arrays (FPGA) Based Computational Complexity Analysis of Multicarrier Waveforms

    C. Ajitha1,*, T. Jaya2

    Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 1033-1048, 2022, DOI:10.32604/iasc.2022.021984

    Abstract Multicarrier waveforms with enhanced spectral efficiency, low latency, and high throughput are required for 5G wireless networks. The Orthogonal Frequency Division Multiplexing (OFDM) method is well-known in research, but due to its limited spectral efficiency, various alternative waveforms are being considered for 5G systems. In the recent communication world, NOMA (non-orthogonal multiple access) plays a significant part due to its wider transmission of data with less bandwidth allocation. Even if a high data rate can be attained, the transmission problem will arise due to the spread of multiple paths. In order to reduce complexity and area utilization, a novel PL-based… More >

  • Open Access


    FPGA Implementation of Deep Leaning Model for Video Analytics

    P. N. Palanisamy*, N. Malmurugan

    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 791-808, 2022, DOI:10.32604/cmc.2022.019921

    Abstract In recent years, deep neural networks have become a fascinating and influential research subject, and they play a critical role in video processing and analytics. Since, video analytics are predominantly hardware centric, exploration of implementing the deep neural networks in the hardware needs its brighter light of research. However, the computational complexity and resource constraints of deep neural networks are increasing exponentially by time. Convolutional neural networks are one of the most popular deep learning architecture especially for image classification and video analytics. But these algorithms need an efficient implement strategy for incorporating more real time computations in terms of… More >

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