Recent advances in deep neural networks have substantially improved the accuracy and speed of various intelligent applications. Nevertheless, one obstacle is that DNN inference imposes a heavy computation burden on end devices, but offloading inference tasks to the cloud causes a large volume of data transmission. Motivated by the fact that the data size of some intermediate DNN layers is significantly smaller than that of raw input data, we designed the DNN surgery, which allows partitioned DNN to be processed at both the edge and cloud while limiting the data transmission. The challenge is twofold: (1) Network dynamics substantially influence the performance of DNN partition, and (2) State-of-the-art DNNs are characterized by a directed acyclic graph rather than a chain, so that partition is incredibly complicated. To solve the issues, We design a Dynamic Adaptive DNN Surgery(DADS) scheme, which optimally partitions the DNN under different network conditions. We also study the partition problem under the cost-constrained system, where the resource of the cloud for inference is limited. Then, a real-world prototype based on the selif-driving car video dataset is implemented, showing that compared with current approaches, DNN surgery can improve latency up to 6.45 times and improve throughput up to 8.31 times. We further evaluate DNN surgery through two case studies where we use DNN surgery to support an indoor intrusion detection application and a campus traffic monitor application, and DNN surgery shows consistently high throughput and low latency.

With the rapid evolution of the Internet, Internet of Things (IoT), and geographic information systems (GIS), spatio-temporal Big Data (STBD) is experiencing exponential growth, marking the onset of the STBD era. Recent studies have concentrated on developing algorithms and techniques for the collection, management, storage, processing, analysis, and visualization of STBD. Researchers have made significant advancements by enhancing STBD handling techniques, creating novel systems, and integrating spatio-temporal support into existing systems. However, these studies often neglect resource management and system optimization, crucial factors for enhancing the efficiency of STBD processing and applications. Additionally, the transition of STBD to the innovative Cloud-Edge-End unified computing system needs to be noticed. In this survey, we comprehensively explore the entire ecosystem of STBD analytics systems. We delineate the STBD analytics ecosystem and categorize the technologies used to process GIS data into five modules: STBD, computation resources, processing platform, resource management, and applications. Specifically, we subdivide STBD and its applications into geoscience-oriented and human-social activity-oriented. Within the processing platform module, we further categorize it into the data management layer (DBMS-GIS), data processing layer (BigData-GIS), data analysis layer (AI-GIS), and cloud native layer (Cloud-GIS). The resource management module and each layer in the processing platform are classified into three categories: task-oriented, resource-oriented, and cloud-based. Finally, we propose research agendas for potential future developments.

Video analytics systems conduct video preprocessing to filter out unnecessary frames and model inference using appropriately selected neural networks for high analytics speed. Video preprocessing is instruction-intensive computing (IIC) executed by CPU, and model inference is data-intensive computing (DIC) executed by GPU. In this paper, we show the analytics accuracy of existing systems can largely vary in fields, caused by the dynamic IIC and DIC workloads of different contents in applications. Unfortunately, cameras have fixed CPU/GPU resources and cannot effectively adapt to workload dynamics. We develop Gemini, a new edge-side real-time video analytics system enhanced by a dual-image FPGA. We take the advantage of negligible image switching time of dual-image FPGAs, pre-configure one CPU image and one GPU image and elastically multiplex the dual CPU-GPU resources in time dimension. Gemini requires both hardware and software revisions. In hardware, we overcome challenges of hardware-dependent application development, low communication efficiency between the microprocessor and FPGA, and high programming complexity by hardware abstraction, asynchronous data transfer mechanism and stub-skeleton middleware. In software, we overcome the challenge of adapting to the dynamic workloads by a bandit learning approach. We implement Gemini and show that Gemini can improve the analytics accuracy to 90.35%.

Federated unlearning (FU) has become an important area of research due to an increasing need for federated learning (FL) applications to comply with emerging data privacy regulations such as GDPR. It facilitates the removal of certain clients' data from an already trained FL model while preserving the performance on the remaining client without the need to retrain from scratch. Existing FU methods typically require clients to have access to their training data or historical model updates, which may be impractical in real-world scenarios due to privacy constraints and changes in data availability. Moreover, FU methods may cause catastrophic unlearning, where removing a client's data from heterogeneous, non-IID settings can negatively impact the model's performance on data from retained clients. To address the aforementioned issues and leverage the capabilities of personalized federated learning (pFL) in handling non-IID data distributions, this paper introduce Mimir, a novel data-free federated unlearning framework designed for pFL settings. Mimir integrates both learning and unlearning phases by utilizing personalized prompts for each client. We design a distillation structure based on Generative Adversarial Networks (GANs) for client-level unlearning that does not require access to original data or historical updates. By leveraging client-specific prompts generated during the pFL phase, Mimir adapts to heterogeneous data distributions and mitigates catastrophic unlearning on the retained data. We demonstrate the effectiveness of Mimir through extensive experiments on benchmark datasets, showing its ability to forget target client data while preserving model accuracy on the remaining clients.

Internet of Things (IoT) gateways integrate various sensors and compute initial decisions before transmitting data to the cloud for further processing. As the functions they need to support become increasingly complex, gateways must upgrade their hardware. Network functions (NF) and video analytics (VAs) are two typical examples of hardware requirements: NFs need specialized hardware accelerators, while VAs need parallel processing power. However, gateways are typically constrained by factors, such as power, size, and cost, leading to a need to multiplex functions and minimize hardware overprovisioning. This article proposes a novel accelerator, the transmission-analytic processing unit (TAPU), which uses multi-image FPGA to accelerate VAs and NFs for IoT gateways. We preconfigure one image for VAs and one image for NFs, then multiplex the FPGA resources in the time dimension. The TAPU system design requires both hardware and software revisions. In the hardware design, we discuss our considerations on hardware choice and present a new abstraction of hardware functions to overcome the challenge of application development on different multi-image FPGAs. For the software, we develop a fully functional TAPU system to adapt to dynamic network and VAs workloads. Our evaluation shows that TAPU utilization can reach 92%, considerably increasing VAs and network processing throughput over the current approach. We further evaluate TAPU through two case studies that support a campus traffic monitoring system and an office surveillance system, demonstrating excellent performance improvement and low overhead.

Consistent thickness in aluminum strips stands as a pivotal indicator of aluminum sheet product quality. Conventional automatic gauge control systems are complex, multivariable, and strongly coupled. However, the rolling process faces uncertainties, preventing the establishment of precise mathematical models. To tackle this, we propose an aluminum strip cold rolling thickness control method grounded in offline reinforcement learning. To facilitate the learning of better control policies, we construct a dataset of aluminum strip cold rolling process control data derived from real-world historical records and expertise for offline policy training, which is named dataset for aluminum strip cold rolling and comprises 8 373 540 Markov decision process tuples. We employ a clustered approach to handle time-varying production conditions. A constrained filtering scheme is introduced to eliminate problematic data after a data-driven ensemble rolling model is established. Evaluation and case study demonstrate that our method effectively reduces aluminum strip thickness deviations without requiring prior knowledge, thus improving control performance.