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,...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 handling the videos in the hardware.Field 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 complexity.But still,an intelligent architecture is required for implementing the CNN in FPGA for processing the videos.This paper introduces a modern high-performance,energy-efficient Bat Pruned Ensembled Convolutional networks(BPEC-CNN)for processing the video in the hardware.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 FPGA.In addition,the proposed system adopts the hardware-software co-design methodology for an energy efficiency and less computational complexity.The 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 analyzed.These results are then compared with the existing pruned CNN architectures such as CNN-Prunner in which the proposed architecture has been shown 25%better performance than the existing architectures.展开更多
Wireless Sensor Network is considered as the intermediate layer in the paradigm of Internet of things(IoT)and its effectiveness depends on the mode of deployment without sacrificing the performance and energy efficien...Wireless Sensor Network is considered as the intermediate layer in the paradigm of Internet of things(IoT)and its effectiveness depends on the mode of deployment without sacrificing the performance and energy efficiency.WSN provides ubiquitous access to location,the status of different entities of the environment and data acquisition for long term IoT monitoring.Achieving the high performance of the WSN-IoT network remains to be a real challenge since the deployment of these networks in the large area consumes more power which in turn degrades the performance of the networks.So,developing the robust and QoS(quality of services)aware energy-efficient routing protocol for WSN assisted IoT devices needs its brighter light of research to enhance the network lifetime.This paper proposed a Hybrid Energy Efficient Learning Protocol(HELP).The proposed protocol leverages the multi-tier adaptive framework to minimize energy consumption.HELP works in a two-tier mechanism in which it integrates the powerful Extreme Learning Machines for clustering framework and employs the zonal based optimization technique which works on hybrid Whale-dragonfly algorithms to achieve high QoS parameters.The proposed framework uses the sub-area division algorithm to divide the network area into different zones.Extreme learning machines(ELM)which are employed in this framework categories the Zone’s Cluster Head(ZCH)based on distance and energy.After categorizing the zone’s cluster head,the optimal routing path for an energy-efficient data transfer will be selected based on the new hybrid whale-swarm algorithms.The extensive simulations were carried out using OMNET++-Python userdefined plugins by injecting the dynamic mobility models in networks to make it a more realistic environment.Furthermore,the effectiveness of the proposed HELP is examined against the existing protocols such as LEACH,M-LEACH,SEP,EACRP and SEEP and results show the proposed framework has outperformed other techniques in terms of QoS parameters such as network lifetime,energy,latency.展开更多
文摘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 handling the videos in the hardware.Field 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 complexity.But still,an intelligent architecture is required for implementing the CNN in FPGA for processing the videos.This paper introduces a modern high-performance,energy-efficient Bat Pruned Ensembled Convolutional networks(BPEC-CNN)for processing the video in the hardware.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 FPGA.In addition,the proposed system adopts the hardware-software co-design methodology for an energy efficiency and less computational complexity.The 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 analyzed.These results are then compared with the existing pruned CNN architectures such as CNN-Prunner in which the proposed architecture has been shown 25%better performance than the existing architectures.
文摘Wireless Sensor Network is considered as the intermediate layer in the paradigm of Internet of things(IoT)and its effectiveness depends on the mode of deployment without sacrificing the performance and energy efficiency.WSN provides ubiquitous access to location,the status of different entities of the environment and data acquisition for long term IoT monitoring.Achieving the high performance of the WSN-IoT network remains to be a real challenge since the deployment of these networks in the large area consumes more power which in turn degrades the performance of the networks.So,developing the robust and QoS(quality of services)aware energy-efficient routing protocol for WSN assisted IoT devices needs its brighter light of research to enhance the network lifetime.This paper proposed a Hybrid Energy Efficient Learning Protocol(HELP).The proposed protocol leverages the multi-tier adaptive framework to minimize energy consumption.HELP works in a two-tier mechanism in which it integrates the powerful Extreme Learning Machines for clustering framework and employs the zonal based optimization technique which works on hybrid Whale-dragonfly algorithms to achieve high QoS parameters.The proposed framework uses the sub-area division algorithm to divide the network area into different zones.Extreme learning machines(ELM)which are employed in this framework categories the Zone’s Cluster Head(ZCH)based on distance and energy.After categorizing the zone’s cluster head,the optimal routing path for an energy-efficient data transfer will be selected based on the new hybrid whale-swarm algorithms.The extensive simulations were carried out using OMNET++-Python userdefined plugins by injecting the dynamic mobility models in networks to make it a more realistic environment.Furthermore,the effectiveness of the proposed HELP is examined against the existing protocols such as LEACH,M-LEACH,SEP,EACRP and SEEP and results show the proposed framework has outperformed other techniques in terms of QoS parameters such as network lifetime,energy,latency.
