Arbitrary-Oriented Object Detection (AOOD) has found broad applications in embodied intelligence, autonomous driving, and satellite remote sensing. However, current AOOD frameworks face challenges in ineffective feature extraction and orientation regression inaccuracy. Inspired by Hilbert curve's intrinsic locality-preserving property, we propose a flexible Hilbert curve-Encoded Rotation-Equivariant Oriented Object Detector (HERO-Det). Our innovations include: (i) a novel Hilbert curve traversal convolution paradigm with a dimensionality reduction scheme, which employs locality-preserving spatial filling curves for feature transformation, (ii) a Hilbert pyramid transformer enabling hierarchical construction of multi-scale feature sequences through space-folding operations, as well as (iii) an orientation-adaptive prediction head that decouples rotation-equivariant regression features from invariant classification cues to resolve orientation regression dilemmas in two-stage detectors. Extensive experiments show HERO-Det achieves state-of-the-art performance on AOOD benchmarks, with mAP of 79.56%, 90.64%, 90.10%, and 80.47% on DOTA, HRSC2016, SSDD, and HRSID, respectively. Performance gains in cross-task validation further demonstrate the versatility of our method to diverse vision tasks, such as medical image segmentation and 3D object detection.

In collaborative inference scenarios, semantic communication replaces raw data transmission by conveying task-oriented semantic features to improve bandwidth efficiency. However, under noisy wireless channels, the combined effects of semantic compression distortion and channel noise lead to severe information loss, resulting in degraded inference accuracy. To address this issue, this paper proposes a Multi-scale Semantic Compression Collaborative Inference (MSCCI) framework that achieves efficient, stable inference performance under high compression ratios and elevated noise levels. Specifically, a UNet-based encoder extracts multi-scale semantic features on an IoT device. These features are then integrated into a unified stream using a novel semantic fusion compression strategy, thereby substantially reducing communication overhead. The edge server decoder decompresses features and recovers image semantics via progressive upsampling and multi-scale semantic restoration. For noisy wireless channels, the framework incorporates Squeeze-and-Excite (SE) attention for dynamic feature channel weighting and residual connections for enhanced low-SNR robustness. Experimental results demonstrate that our collaborative inference framework for semantic communication outperforms state-of-the-art algorithms, and the approach’s effectiveness and robustness are verified across various channel conditions.

Edge computing deploys computing and storage resources at the network edge, thereby providing services closer to terminal users. However, in edge networks, the mobility of terminals, the diversity of requests, and the dynamic nature of wireless channels pose significant challenges for efficiently allocating limited wireless and caching resources among multiple terminal devices. To address the issues of unbalanced network load and high caching costs caused by resource allocation in edge networks, we propose a Distributed Online Joint Optimization Scheme (DOJS). Specifically, we design a joint optimization scheme, referred to as DOJS, which combines centralized user association at the cloud with distributed cache placement at the base stations. This scheme analyzes the impact of terminal device association policies on caching costs and develops a caching cost model that integrates the activity level and content request probability of terminal devices. Based on this model, the relationship between user association selection and caching costs is analyzed, and a Game Theory-based User Association (GTUA) selection algorithm is proposed. In order to adapt to the dynamic characteristics of terminal-user requests in mobile edge networks, we develop a dynamic cache update method LS-TD3, which combines Long Short-Term Memory (LSTM) and Twin Delayed Deep Deterministic policy gradient (TD3). Specifically, we integrate the LSTM layer into the policy model framework of reinforcement learning to better predict the content popularity from dynamic time data, thus improving the accuracy of cache decision making. To further reduce computational complexity and enhance overall system performance, we employ a distributed optimization strategy to improve the dynamic caching decision process. Extensive experimental results demonstrate the superiority of the proposed algorithm in achieving inter-node load balancing and minimizing caching costs.

Modern processors rely on advanced memory management techniques such as predicting and prefetching to bridge the processor–memory gap. However, current mechanisms are primarily designed for a specific memory hierarchy level or access pattern, resulting in inefficiencies for diverse workloads with mixed memory access patterns. To address this challenge, this paper proposes DHCM, a Dynamic Hierarchy Coordination Mechanism that intelligently schedules prediction hierarchies and dynamically optimizes memory access processes to enhance system performance. DHCM integrates a hierarchy coordination mechanism driven by a State Trigger. This mechanism dynamically leverages system feedback to prioritize and coordinate memory operations, enabling the simultaneous management of both off-chip load requests and on-chip cache accesses. Through extensive evaluations on the ChampSim simulator, DHCM demonstrates its adaptability and efficiency with an average IPC improvement of 34.08% and 24.09% on single-core and multi-core systems, respectively. Additionally, DHCM contributes a 64.17% miss coverage and 89.33% DRAM-loads reduction.

The rapid advancement of artificial intelligence applications has resulted in the deployment of a growing number of deep neural networks (DNNs) on mobile devices. Given the limited computational capabilities and small battery capacity of these devices, supporting efficient DNN inference presents a significant challenge. This paper considers the joint design of DNN model partitioning and offloading under high-concurrent tasks scenarios. The primary objective is to accelerate DNN task inference and reduce computational delay. Firstly, we propose an innovative adaptive inference framework that partitions DNN models into interdependent sub-tasks through a hierarchical partitioning method. Secondly, we develop a delay prediction model based on a Random Forest (RF) regression algorithm to estimate the computational delay of each sub-task on different devices. Finally, we designed a high-performance DNN partitioning and task offloading method based on an attention mechanism-aided Soft Actor-Critic (AMSAC) algorithm. The bandwidth allocation for each user is determined by the attention mechanism based on the characteristics of the DNN tasks, and the Soft Actor-Critic algorithm is used for adaptive layer-level partitioning and offloading of the DNN model, reducing collaborative inference delay. Extensive experiments demonstrate that our proposed AMSAC algorithm effectively reduces DNN task inference latency cost and improves service quality.

Mobile terminals deploy deep neural networks (DNNs) in order to be able to perform tasks such as target detection and speech recognition. However, mobile terminals have limited computing power and storage space. It is difficult to run DNN-based applications directly on these devices. One solution is to offload some of partitioned tasks to the edge server for execution. Implementing this method is not easy due to two aspects: (1) Partitioning layer selection: a suitable partitioning layer needs to be selected in order to efficiently offload computational tasks to the edge servers. (2) Ineffectiveness of partitioning: the layer partitioning method does not significantly improve operational efficiency of DNNs in many cases, especially when dealing with complex structured DNNs such as Inception neural Network. This paper proposes a novel partitioning method, called INCEPTIONOFF. For a given DNN-based application, the method determines the partitioning layer, automatically determines the offloading scheme. We evaluated it on an image classification task using inception neural network and showed that this partitioning method achieves an average speedup of 205% and the fastest speedup of 263% compared to other methods.

The dynamic mechanism of joint proactive caching and cache replacement, which involves placing content items close to cache-enabled edge devices ahead of time until they are requested, is a promising technique for enhancing traffic offloading and relieving heavy network loads. However, due to limited edge cache capacity and wireless transmission resources, accurately predicting users’ future requests and performing dynamic caching is crucial to effectively utilizing these limited resources. This paper investigates joint proactive caching and cache replacement strategies in a general mobile edge computing (MEC) network with multiple users under a cloud-edge-device collaboration architecture. The joint optimization problem is formulated as a markov decision process (MDP) problem with an infinite range of average network load costs, aiming to reduce network load traffic while efficiently utilizing the limited available transport resources. To address this issue, we design an Attention Weighted Deep Deterministic Policy Gradient (AWD2PG) model, which uses attention weights to allocate the number of channels from server to user, and applies deep deterministic policies on both user and server sides for Cache decision-making, so as to achieve the purpose of reducing network traffic load and improving network and cache resource utilization. We verify the convergence of the corresponding algorithms and demonstrate the effectiveness of the proposed AWD2PG strategy and benchmark in reducing network load and improving hit rate.

The problem of shared node selection and cache placement in wireless networks is challenging due to the difficulty of finding low-complexity optimal solutions. This paper proposes a new approach combining Lyapunov optimization and reinforcement learning (LoRL) to address content sharing in heterogeneous mobile edge computing (MEC) networks with base station (BS) and device-to-device (D2D) communication. Device in this network can choose to establish D2D links with neighboring devices for content sharing or send requests directly to the base station for content. Content access and energy consumption of shared nodes are modeled as a queuing system. The goal is to assign content sharing nodes to stabilize all queues while maximizing D2D sharing gain and minimizing latency, even in the presence of unknown network state distribution and user sharing costs. The proposed approach enables edge device to independently select associated nodes and make caching decisions, thereby minimizing time-averaged network costs and stabilizing the queuing system. Experimental results show that the proposed algorithm converges to the optimal policy and outperforms other policies in terms of total queue backlog trade-off and network cost.