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
Dear Editor,The letter proposes a tensor low-rank orthogonal compression(TLOC)model for a convolutional neural network(CNN),which facilitates its efficient and highly-accurate low-rank representation.Model compression...Dear Editor,The letter proposes a tensor low-rank orthogonal compression(TLOC)model for a convolutional neural network(CNN),which facilitates its efficient and highly-accurate low-rank representation.Model compression is crucial for deploying deep neural network(DNN)models on resource-constrained embedded devices.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
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.
基金supported by the Science and Technology Innovation Key R&D Program of Chongqing(CSTB2025TIAD-STX0032)National Key Research and Development Program of China(2024YFF0908200)+1 种基金the Chongqing Technology Innovation and Application Development Special Key Project(CSTB2024TIAD-KPX0018)the Southwest University Graduate Student Research Innovation(SWUB24051)。
文摘Dear Editor,The letter proposes a tensor low-rank orthogonal compression(TLOC)model for a convolutional neural network(CNN),which facilitates its efficient and highly-accurate low-rank representation.Model compression is crucial for deploying deep neural network(DNN)models on resource-constrained embedded devices.
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%.
