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FPGA Implementation of Deep Leaning Model for Video Analytics

Computers, Materials & Continua 2022年第4期71卷 791-808页

作者： P.N.Palanisamy N.Malmurugan Mahendra College of Engineering SalemIndia

In recent years,deep neural networks have become a fascinating and influential research subject,and they play a critical role in video processing and ***,video analytics are predominantly hardware centric,exploration of implementing the deep neural networks in the hardware needs its brighter light of ***,the computational complexity and resource constraints of deep neural networks are increasing exponentially by *** neural networks are one of the most popular deep learning architecture especially for image classification and video *** these algorithms need an efficient implement strategy for incorporating more real time computations in terms of handling the videos in the *** programmable Gate arrays(FPGA)is thought to be more advantageous in implementing the convolutional neural networks when compared to Graphics Processing Unit(GPU)in terms of energy efficient and low computational *** still,an intelligent architecture is required for implementing the CNN in FPGA for processing the *** paper introduces a modern high-performance,energy-efficient Bat Pruned Ensembled Convolutional networks(BPEC-CNN)for processing the video in the *** system integrates the Bat Evolutionary Pruned layers for CNN and implements the new shared distributed filtering structures(DFS)for handing the filter layers in CNN with pipelined data-path in *** addition,the proposed system adopts the hardware-software co-design methodology for an energy efficiency and less computational *** extensive experimentations are carried out using CASIA video datasets with ARTIX-7 FPGA boards(number)and various algorithms centric parameters such as accuracy,sensitivity,specificity and architecture centric parameters such as the power,area and throughput are *** results are then compared with the existing pruned CNN architectures such as CNN-Prunner in which the proposed architecture has been shown

关键词： Deep neural networks field programmable gate arrays convolutional neural networks distributed filtering structures bat-pruned

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