Related papers: Case Study: Performance Analysis of a Virtualized …

In this report we present a network-level multi-core energy model and a software development process workflow that allows software developers to estimate the energy consumption of multi-core embedded programs. This work focuses on a high…

Three-dimensional (3D) point clouds are increasingly used in applications such as autonomous driving, robotics, and virtual reality (VR). Point-based neural networks (PNNs) have demonstrated strong performance in point cloud analysis,…

Federated Learning (FL) has emerged as a transformative approach for enabling distributed machine learning while preserving user privacy, yet it faces challenges like communication inefficiencies and reliance on centralized infrastructures,…

Real-time energy forecasting on edge devices represents a major challenge for smart grid optimization and intelligent buildings. We present LAD-BNet (Lag-Aware Dual-Branch Network), an innovative neural architecture optimized for edge…

Network Function Virtualization is considered one of the key technologies for developing the future mobile networks. In this paper, we propose a theoretical framework to evaluate the performance of an LTE virtualized Mobility Management…

The advent of 5G virtualized Radio Access Networks (vRANs) brings a new challenge with regards to computer architectures. It requires to select or design computing technologies that provide a sufficient level of performance while maximizing…

Cloud Radio Access Network (C-RAN) is emerging as a transformative architecture for the next generation of mobile cellular networks. In C-RAN, the Baseband Unit (BBU) is decoupled from the Base Station (BS) and consolidated in a centralized…

This work presents an in-depth analysis of the majority of the deep neural networks (DNNs) proposed in the state of the art for image recognition. For each DNN multiple performance indices are observed, such as recognition accuracy, model…

Today, deep learning is an essential technology for our life. To solve more complex problems with deep learning, both sizes of training datasets and neural networks are increasing. To train a model with large datasets and networks,…

Cloud computing has provided economies of scale, savings, and efficiency for both individual consumers and enterprises. Its key advantage is its ability to handle increasing amounts of data and provide functionality that gives users the…

Near-data accelerators (NDAs) that are integrated with main memory have the potential for significant power and performance benefits. Fully realizing these benefits requires the large available memory capacity to be shared between the host…

A Fog radio access network is considered as a network architecture candidate to meet the soaring demand in terms of reliability, spectral efficiency, and latency in next generation wireless networks. This architecture combines the benefits…

Virtual reality (VR) is becoming prevalent with a plethora of applications in education, healthcare, entertainment, etc. To increase the user mobility, and to reduce the energy consumption and production cost of VR head mounted displays…

We design and implement on Radio Frequency System-on-Chip (RFSoC) software-defined radios (SDRs) a complete-graph network of four unmanned aerial vehicles and demonstrate real-time 4K video streaming over twelve always-on 2x2 multiple-input…

We extend our earlier studies of buffer requirements of TCP over ABR in two directions. First, we study the performance of TCP over ABR in an ATM backbone. On the backbone, the TCP queues are at the edge router and not inside the ATM…

Traditionally, applications that are used in large and small enterprises were deployed on "bare metal" servers installed with operating systems. Recently, the use of multiple virtual machines (VMs) on the same physical server was adopted…

We propose a novel solid-state disk (SSD) architecture that utilizes a double-data-rate synchronous NAND flash interface for improving read and write performance. Unlike the conventional design, the data transfer rate in the proposed design…

Deploying large language models (LLMs) on edge devices enables personalized agents with strong privacy and low cost. However, with tens to hundreds of billions of parameters, single-batch autoregressive inference suffers from extremely low…

Delay tolerant network is a network architecture and protocol suite specifically designed to handle challenging communications environments, such as deep space communications, disaster response, and remote area communications. Although DTN…

Binary matrix-vector multiplication (BMVM) is a key operation in post-quantum cryptography schemes like the Classic McEliece cryptosystem. Conventional computing architectures incur significant energy efficiency loss due to data movement of…