Efficient real-time resource allocation for electric vehicle (EV) charging in Fog computing environments demands fast and intelligent decision-making under strict quality-of-service constraints. Traditional metaheuristics like genetic algorithms and differential evolution yield high-quality solutions but incur prohibitive computational costs, limiting their applicability in real-time systems. This paper introduces the Committee-Based Active Learning Surrogate-Assisted Particle Swarm Optimization (QBC-SA-PSO) framework, which combines multiple surrogate models with a Query by Committee (QBC) strategy to intelligently approximate fitness evaluations. By balancing exploration and exploitation, the framework drastically reduces the need for expensive exact simulations while maintaining near-optimal solution quality. Experimental validation on EV charging datasets demonstrates that PSO-SA-QBC converges within only 33 iterations, achieving a 66% reduction compared to traditional simulation techniques replaced with exact fitness evaluations, while preserving over 99% solution quality.

The rapid adoption of electric vehicles (EVs) raises critical challenges in managing charging stations, requiring efficient resource allocation to balance demand, optimize energy use, and maintain grid stability. This paper proposes FL-PSO, a hybrid strategy combining Federated Learning (FL) and Particle Swarm Optimization (PSO) for resource management in fog-cloud computing environments. FL-PSO is applied to EV charging stations and benchmarked against standard PSO, Genetic Algorithm (GA), and Cat Swarm Optimization (CSO). Evaluation is conducted using Quality of Service (QoS) metrics, including latency, energy consumption, and load balancing. Results show that FL-PSO achieves a fitness value of 0.1489, reduced energy consumption (0.2644 W), and improved load balancing (4.29), consistently outperforming baseline methods under both fixed weights and optimal QoS trade-offs. By leveraging the synergy between FL and PSO, FL-PSO provides a scalable and intelligent solution for distributed fog-cloud systems supporting smart EV charging infrastructures.

Abstract Applications running in a large and complex manycore system can significantly benefit from adopting the dataflow model of computation. In a dataflow execution environment, a thread can run only if all its required inputs are available. While the potential benefits are large, it is not trivial to improve resource utilization and energy efficiency by focusing on dataflow thread execution models (i.e., the ways specifying how the threads adhering to a dataflow model of computation execute on a given compute/communication architecture). This paper proposes and implements a hardware-software co-design-based dataflow threads management framework. It works at the Network-on-Chip (NoC) level and consists of three stages. The first stage focuses on a fast and effective thread distribution policy. The next stage proposes an approach that adds reconfigurability to a 2D mesh NoC via customized instructions to manage the dataflow thread distribution. Finally, a 2D mesh and ring-based hybrid NoC is proposed for better scalability and higher performance. This work can be considered a primary reference framework from which extensions can be carried out.

One key technical challenge in the age of autonomous machines is the programming of autonomous machines, which demands the synergy across multiple domains, including fundamental computer science, computer architecture, and robotics, and requires expertise from both academia and industry. This paper discusses the programming theory and practices tied to producing real-life autonomous machines, and covers aspects from high-level concepts down to low-level code generation in the context of specific functional requirements, performance expectation, and implementation constraints of autonomous machines.

Introduction: Teaching autonomous driving is a challenging task. Indeed, most existing autonomous driving teaching activities focus on a few of the technologies involved. This not only fails to provide a comprehensive coverage, but also sets a high entry barrier for students with different backgrounds. Objective: The primary objective of this study is to present a modular, integrated approach towards teaching autonomous driving. Methods: We organize the technologies used in autonomous driving into modules. This is described in the textbook we have developed as well as a series of multimedia online lectures designed to provide technical overview for each module. Once the students have understood these modules, the experimental platforms for integration we have developed allow the students to fully understand how the modules interact with each other. Results: To verify this teaching approach, we present three case studies: an introductory class on autonomous driving for students with only a basic technology background; a new session in an existing embedded systems class to demonstrate how embedded system technologies can be applied towards autonomous driving; and an industry professional training session to quickly bring up experienced engineers to work in autonomous driving. The results show that students can maintain a high interest level and make great progress by starting with familiar concepts before moving onto other modules. Conclusions: Autonomous driving is not one single technology, but rather a complex system integrating many technologies. Our modular and integrated approach is an effective method in teaching autonomous driving.

The overwhelming wealth of parallelism exposed by Extreme-scale computing is rekindling the interest for finegrain multithreading, particularly at the intranode level. Indeed, popular parallel programming models, such as OpenMP, are integrating fine-grain tasking in their newest standards. Yet, classical coarse-grain constructs are still largely preferred, as they are considered simpler to express parallelism. In this paper, we present a Multigrain Parallel Programming environment that allows programmers to use these well-known coarse-grain constructs to generate a fine-grain multithreaded application to be run on top of a fine-grain event-driven program execution model. Experimental results with four scientific benchmarks (Graph500, NAS Data Cube, NWChem-SCF, and ExMatEx's CoMD) show that fine-grain applications generated by and run on our environment are competitive and even outperform their OpenMP counterparts, especially for data-intensive workloads with irregular and dynamic parallelism, reaching speedups as high as 2.6x for Graph500 and 50x for NAS Data Cube.

The recent evolution in hardware landscape, aimed at producing high-performance computing systems capable of reaching extreme-scale performance, has reignited the interest in fine-grain multithreading, particularly at the intranode level. Indeed, popular parallel programming environments, such as OpenMP, which features a simple interface for the parallelization of programs, are now incorporating fine-grain constructs. However, since coarse-grain directives are still heavily used, the OpenMP runtime is forced to support both coarse- and fine-grain models of execution, potentially reducing the advantages obtained when executing an application in a fully fine-grain environment. To evaluate the type of applications that benefit from executing in a unified fine-grain program execution model, this article presents amultigrain parallel programming environmentfor the generation of fine-grain multithreaded applications from programs featuring OpenMP’s API, allowing OpenMP programs to be run on top of a fine-grain event-driven program execution model. Experimental results with five scientific benchmarks show that fine-grain applications, generated by and run on our environment with two runtimes implementing a fine-grain event-driven program execution model, are competitive and can outperform their OpenMP counterparts, especially for data-intensive workloads with irregular and dynamic parallelism, reaching speedups as high as 2.6× for Graph500 and 51× for NAS Data Cube.

Thanks to the improvements in semiconductor technologies, extreme-scale systems such as teradevices (i.e., composed by 1000 billion of transistors) will enable systems with 1000+ general purpose cores per chip, probably by 2020. Three major challenges have been identified: programmability, manageable architecture design, and reliability. TERAFLUX is a Future and Emerging Technology (FET) large-scale project funded by the European Union, which addresses such challenges at once by leveraging the dataflow principles. This paper describes the project and provides an overview of the research carried out by the TERAFLUX consortium.