Related papers: GenioSim: A Novel Simulation Platform for Edge Com…
The Internet of Things (IoT) has been increasingly used in our everyday lives as well as in numerous industrial applications. However, due to limitations in computing and power capabilities, IoT devices need to send their respective tasks…
Many cloud-based applications employ a data centre as a central server to process data that is generated by edge devices, such as smartphones, tablets and wearables. This model places ever increasing demands on communication and…
The Internet of Drones (IoD) is a networking architecture that stems from the interplay between Unmanned Aerial Vehicles (UAVs) and wireless communication technologies. Networked drones can unleash disruptive scenarios in many application…
Oscillator-based Ising/Potts machines (OIMs/OPMs) are promising hardware accelerators for NP-hard combinatorial optimization problems using coupled oscillator synchronization dynamics. Analog OIMs/OPMs offer speed advantages but have…
Modern digital engineering design process commonly involves expensive repeated simulations on varying three-dimensional (3D) geometries. The efficient prediction capability of neural networks (NNs) makes them a suitable surrogate to provide…
The number of satellites, especially those operating in low-earth orbit (LEO), is exploding in recent years. Additionally, the use of COTS hardware into those satellites enables a new paradigm of computing: orbital edge computing (OEC). OEC…
Edge computing was introduced as a technical enabler for the demanding requirements of new network technologies like 5G. It aims to overcome challenges related to centralized cloud computing environments by distributing computational…
As robotic navigation techniques in perception and planning advance, mobile robots increasingly venture into off-road environments involving complex traversability. However, selecting suitable planning methods remains a challenge due to…
This paper focuses on the simulation of multi-die System-on-Chip (SoC) architectures using VisualSim, emphasizing chiplet-based system modeling and performance analysis. Chiplet technology presents a promising alternative to traditional…
Fog computing plays a crucial role in satisfying the requirements of delay-sensitive applications such as connected vehicles, smart grids, and actuator networks by moving data processing close to end users. Passive optical networks (PONs)…
In this paper, we consider a hybrid mobile edge computing (H-MEC) platform, which includes ground stations (GSs), ground vehicles (GVs) and unmanned aerial vehicle (UAVs), all with mobile edge cloud installed to enable user equipments (UEs)…
In modern power systems, edge devices serve as local hubs that collect data, perform on-site computing, sense electrical parameters, execute control actions, and communicate with neighboring edge devices as part of the larger grid. However,…
Large-scale tensor network simulations are crucial for developing robust complexity-theoretic bounds on classical quantum simulation, enabling circuit cutting approaches, and optimizing circuit compilation, all of which aid efficient…
Machine learning-based simulators offer the potential to model the dynamics of complex systems more efficiently than classical approaches, while retaining differentiability, a key property for materials design. Graph neural network…
Significant challenges are posed by simulation and testing in the field of low-altitude unmanned aerial vehicle (UAV) traffic due to the high costs associated with large-scale UAV testing and the complexity of establishing low-altitude…
The proliferation of the Internet of Things (IoT) and its cutting-edge AI-enabled applications (e.g., autonomous vehicles and smart industries) combine two paradigms: data-driven systems and their deployment on the edge. Usually, edge…
Emerging computation-intensive applications impose stringent latency requirements on resource-constrained mobile devices. Mobile Edge Computing (MEC) addresses this challenge through task offloading. However, designing effective policies…
The optical neural network (ONN) is a promising hardware platform for next-generation neurocomputing due to its high parallelism, low latency, and low energy consumption. Previous ONN architectures are mainly designed for general matrix…
The rapid evolution of communication technologies such as 5G and beyond, rely on optical networks to support the challenging and ambitious requirements that include both capacity and reliability. This chapter begins by giving an overview of…
Optical neural network (ONN) is emerging as an attractive proposal for machine-learning applications, enabling high-speed computation with low-energy consumption. However, there are several challenges in applying ONN for industrial…