针对深度神经网络(deep neural network,DNN)模型在传统切片与映射方法中存在的资源调度和数据传输瓶颈问题,提出了一种基于片上网络(network on chip,NoC)加速器的高效DNN动态切片与智能映射优化算法。该算法通过动态切片技术灵活划分...针对深度神经网络(deep neural network,DNN)模型在传统切片与映射方法中存在的资源调度和数据传输瓶颈问题,提出了一种基于片上网络(network on chip,NoC)加速器的高效DNN动态切片与智能映射优化算法。该算法通过动态切片技术灵活划分DNN模型的计算任务,并结合智能映射策略优化NoC架构中的任务分配与数据流管理。实验结果表明,与传统方法相比,该算法在计算吞吐量、NoC传输时延、外部内存访问次数和计算能效等方面均显著提升,尤其在复杂模型上表现突出。展开更多
Deep neural network(DNN)models have achieved remarkable performance across diverse tasks,leading to widespread commercial adoption.However,training high-accuracy models demands extensive data,substantial computational...Deep neural network(DNN)models have achieved remarkable performance across diverse tasks,leading to widespread commercial adoption.However,training high-accuracy models demands extensive data,substantial computational resources,and significant time investment,making them valuable assets vulnerable to unauthorized exploitation.To address this issue,this paper proposes an intellectual property(IP)protection framework for DNN models based on feature layer selection and hyper-chaotic mapping.Firstly,a sensitivity-based importance evaluation algorithm is used to identify the key feature layers for encryption,effectively protecting the core components of the model.Next,the L1 regularization criterion is applied to further select high-weight features that significantly impact the model’s performance,ensuring that the encryption process minimizes performance loss.Finally,a dual-layer encryption mechanism is designed,introducing perturbations into the weight values and utilizing hyperchaotic mapping to disrupt channel information,further enhancing the model’s security.Experimental results demonstrate that encrypting only a small subset of parameters effectively reduces model accuracy to random-guessing levels while ensuring full recoverability.The scheme exhibits strong robustness against model pruning and fine-tuning attacks and maintains consistent performance across multiple datasets,providing an efficient and practical solution for authorization-based DNN IP protection.展开更多
Various intelligent applications based on non-chain DNN models are widely used in Internet of Things(IoT)scenarios.However,resource-constrained Io T devices usually cannot afford the heavy computation burden and canno...Various intelligent applications based on non-chain DNN models are widely used in Internet of Things(IoT)scenarios.However,resource-constrained Io T devices usually cannot afford the heavy computation burden and cannot guarantee the strict inference latency requirements of non-chain DNN models.Multi-device collaboration has become a promising paradigm for achieving inference acceleration.However,existing works neglect the possibility of inter-layer parallel execution,which fails to exploit the parallelism of collaborating devices and inevitably prolongs the overall completion latency.Thus,there is an urgent need to pay attention to the issue of non-chain DNN inference acceleration with multi-device collaboration based on inter-layer parallel.Three major challenges to be overcome in this problem include exponential computational complexity,complicated layer dependencies,and intractable execution location selection.To this end,we propose a Topological Sorting Based Bidirectional Search(TSBS)algorithm that can adaptively partition non-chain DNN models and select suitable execution locations at layer granularity.More specifically,the TSBS algorithm consists of a topological sorting subalgorithm to realize parallel execution with low computational complexity under complicated layer parallel constraints,and a bidirectional search subalgorithm to quickly find the suitable execution locations for non-parallel layers.Extensive experiments show that the TSBS algorithm significantly outperforms the state-of-the-arts in the completion latency of non-chain DNN inference,a reduction of up to 22.69%.展开更多
As the scale of the networks continually expands,the detection of distributed denial of service(DDoS)attacks has become increasingly vital.We propose an intelligent detection model named IGED by using improved general...As the scale of the networks continually expands,the detection of distributed denial of service(DDoS)attacks has become increasingly vital.We propose an intelligent detection model named IGED by using improved generalized entropy and deep neural network(DNN).The initial detection is based on improved generalized entropy to filter out as much normal traffic as possible,thereby reducing data volume.Then the fine detection is based on DNN to perform precise DDoS detection on the filtered suspicious traffic,enhancing the neural network’s generalization capabilities.Experimental results show that the proposed method can efficiently distinguish normal traffic from DDoS traffic.Compared with the benchmark methods,our method reaches 99.9%on low-rate DDoS(LDDoS),flooded DDoS and CICDDoS2019 datasets in terms of both accuracy and efficiency in identifying attack flows while reducing the time by 17%,31%and 8%.展开更多
Unmanned aerial vehicle(UAV)-enabled edge computing is emerging as a potential enabler for Artificial Intelligence of Things(AIoT)in the forthcoming sixth-generation(6G)communication networks.With the use of flexible ...Unmanned aerial vehicle(UAV)-enabled edge computing is emerging as a potential enabler for Artificial Intelligence of Things(AIoT)in the forthcoming sixth-generation(6G)communication networks.With the use of flexible UAVs,massive sensing data is gathered and processed promptly without considering geographical locations.Deep neural networks(DNNs)are becoming a driving force to extract valuable information from sensing data.However,the lightweight servers installed on UAVs are not able to meet the extremely high requirements of inference tasks due to the limited battery capacities of UAVs.In this work,we investigate a DNN model placement problem for AIoT applications,where the trained DNN models are selected and placed on UAVs to execute inference tasks locally.It is impractical to obtain future DNN model request profiles and system operation states in UAV-enabled edge computing.The Lyapunov optimization technique is leveraged for the proposed DNN model placement problem.Based on the observed system overview,an advanced online placement(AOP)algorithm is developed to solve the transformed problem in each time slot,which can reduce DNN model transmission delay and disk I/O energy cost simultaneously while keeping the input data queues stable.Finally,extensive simulations are provided to depict the effectiveness of the AOP algorithm.The numerical results demonstrate that the AOP algorithm can reduce 18.14%of the model placement cost and 29.89%of the input data queue backlog on average by comparing it with benchmark algorithms.展开更多
文摘针对深度神经网络(deep neural network,DNN)模型在传统切片与映射方法中存在的资源调度和数据传输瓶颈问题,提出了一种基于片上网络(network on chip,NoC)加速器的高效DNN动态切片与智能映射优化算法。该算法通过动态切片技术灵活划分DNN模型的计算任务,并结合智能映射策略优化NoC架构中的任务分配与数据流管理。实验结果表明,与传统方法相比,该算法在计算吞吐量、NoC传输时延、外部内存访问次数和计算能效等方面均显著提升,尤其在复杂模型上表现突出。
基金supported in part by the National Natural Science Foundation of China under Grant No.62172280in part by the Key Scientific Research Projects of Colleges and Universities in Henan Province,China under Grant No.23A520006in part by Henan Provincial Science and Technology Research Project under Grant No.222102210199.
文摘Deep neural network(DNN)models have achieved remarkable performance across diverse tasks,leading to widespread commercial adoption.However,training high-accuracy models demands extensive data,substantial computational resources,and significant time investment,making them valuable assets vulnerable to unauthorized exploitation.To address this issue,this paper proposes an intellectual property(IP)protection framework for DNN models based on feature layer selection and hyper-chaotic mapping.Firstly,a sensitivity-based importance evaluation algorithm is used to identify the key feature layers for encryption,effectively protecting the core components of the model.Next,the L1 regularization criterion is applied to further select high-weight features that significantly impact the model’s performance,ensuring that the encryption process minimizes performance loss.Finally,a dual-layer encryption mechanism is designed,introducing perturbations into the weight values and utilizing hyperchaotic mapping to disrupt channel information,further enhancing the model’s security.Experimental results demonstrate that encrypting only a small subset of parameters effectively reduces model accuracy to random-guessing levels while ensuring full recoverability.The scheme exhibits strong robustness against model pruning and fine-tuning attacks and maintains consistent performance across multiple datasets,providing an efficient and practical solution for authorization-based DNN IP protection.
基金supported by the National Key Research and Development Program of China(2021YFB2900102)the National Natural Science Foundation of China(No.62072436 and No.62202449)。
文摘Various intelligent applications based on non-chain DNN models are widely used in Internet of Things(IoT)scenarios.However,resource-constrained Io T devices usually cannot afford the heavy computation burden and cannot guarantee the strict inference latency requirements of non-chain DNN models.Multi-device collaboration has become a promising paradigm for achieving inference acceleration.However,existing works neglect the possibility of inter-layer parallel execution,which fails to exploit the parallelism of collaborating devices and inevitably prolongs the overall completion latency.Thus,there is an urgent need to pay attention to the issue of non-chain DNN inference acceleration with multi-device collaboration based on inter-layer parallel.Three major challenges to be overcome in this problem include exponential computational complexity,complicated layer dependencies,and intractable execution location selection.To this end,we propose a Topological Sorting Based Bidirectional Search(TSBS)algorithm that can adaptively partition non-chain DNN models and select suitable execution locations at layer granularity.More specifically,the TSBS algorithm consists of a topological sorting subalgorithm to realize parallel execution with low computational complexity under complicated layer parallel constraints,and a bidirectional search subalgorithm to quickly find the suitable execution locations for non-parallel layers.Extensive experiments show that the TSBS algorithm significantly outperforms the state-of-the-arts in the completion latency of non-chain DNN inference,a reduction of up to 22.69%.
基金This work is supported by the National Natural Science Foundation of China(Grant Nos.U22B2005,62072109)the Natural Science Foundation of Fujian Province(Grant No.2021J01625)the Major Science and Technology Project of Fuzhou(Grant No.2023-ZD-003).
文摘As the scale of the networks continually expands,the detection of distributed denial of service(DDoS)attacks has become increasingly vital.We propose an intelligent detection model named IGED by using improved generalized entropy and deep neural network(DNN).The initial detection is based on improved generalized entropy to filter out as much normal traffic as possible,thereby reducing data volume.Then the fine detection is based on DNN to perform precise DDoS detection on the filtered suspicious traffic,enhancing the neural network’s generalization capabilities.Experimental results show that the proposed method can efficiently distinguish normal traffic from DDoS traffic.Compared with the benchmark methods,our method reaches 99.9%on low-rate DDoS(LDDoS),flooded DDoS and CICDDoS2019 datasets in terms of both accuracy and efficiency in identifying attack flows while reducing the time by 17%,31%and 8%.
基金supported by the National Science Foundation of China(Grant No.62202118)the Top-Technology Talent Project from Guizhou Education Department(Qianjiao Ji[2022]073)+1 种基金the Natural Science Foundation of Hebei Province(Grant No.F2022203045 and F2022203026)the Central Government Guided Local Science and Technology Development Fund Project(Grant No.226Z0701G).
文摘Unmanned aerial vehicle(UAV)-enabled edge computing is emerging as a potential enabler for Artificial Intelligence of Things(AIoT)in the forthcoming sixth-generation(6G)communication networks.With the use of flexible UAVs,massive sensing data is gathered and processed promptly without considering geographical locations.Deep neural networks(DNNs)are becoming a driving force to extract valuable information from sensing data.However,the lightweight servers installed on UAVs are not able to meet the extremely high requirements of inference tasks due to the limited battery capacities of UAVs.In this work,we investigate a DNN model placement problem for AIoT applications,where the trained DNN models are selected and placed on UAVs to execute inference tasks locally.It is impractical to obtain future DNN model request profiles and system operation states in UAV-enabled edge computing.The Lyapunov optimization technique is leveraged for the proposed DNN model placement problem.Based on the observed system overview,an advanced online placement(AOP)algorithm is developed to solve the transformed problem in each time slot,which can reduce DNN model transmission delay and disk I/O energy cost simultaneously while keeping the input data queues stable.Finally,extensive simulations are provided to depict the effectiveness of the AOP algorithm.The numerical results demonstrate that the AOP algorithm can reduce 18.14%of the model placement cost and 29.89%of the input data queue backlog on average by comparing it with benchmark algorithms.
