FPGA Implementation of Deep Leaning Model for Video Analytics

作     者：P.N.Palanisamy N.Malmurugan 

作者机构：Mahendra College of EngineeringSalemIndia 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2022年第71卷第4期

页      面：791-808页

核心收录：

学科分类：0710[理学-生物学] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 0835[工学-软件工程] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Deep neural networks field programmable gate arrays convolutional neural networks distributed filtering structures bat-pruned 

摘      要：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