Related papers: MiddleNet: A Unified, High-Performance NFV and Mid…

A key feature of the packet scheduler in LTE system is that it can allocate resources both in the time and frequency domain. Furthermore, the scheduler is acquainted with channel state information periodically reported by user equipments…

In this paper, we propose a novel centralized Asynchronous Federated Learning (FL) framework, FAVANO, for training Deep Neural Networks (DNNs) in resource-constrained environments. Despite its popularity, ``classical'' federated learning…

Network Function Virtualization (NFV) is a promising technology that promises to significantly reduce the operational costs of network services by deploying virtualized network functions (VNFs) to commodity servers in place of dedicated…

In the last decade, specific-purpose computing and storage devices, such as GPUs, TPUs, or high-speed storage, have been incorporated into server nodes of Supercomputers and Data centers. The development of high-bandwidth memory (HBM)…

Residual networks (ResNets) have been utilized for various computer vision and image processing applications. The residual connection improves the training of the network with better gradient flow. A residual block consists of few…

We present Net2Vec, a flexible high-performance platform that allows the execution of deep learning algorithms in the communication network. Net2Vec is able to capture data from the network at more than 60Gbps, transform it into meaningful…

Traditionally, Network Function Virtualization (NFV) has been implemented to run on Virtual Machines (VMs) in form of Virtual Network Functions (VNFs). More recently, the so-called Serverless Computing has gained traction in cloud…

Visible Light Ad Hoc Networks (LANETs) is being perceived as an emerging technology to complement Radio Frequency (RF) based ad hoc networks to reduce congestion in the overloaded RF spectrum. LANET is intended to support scenarios…

Federated learning (FL) is a privacy-preserving machine learning setting that enables many devices to jointly train a shared global model without the need to reveal their data to a central server. However, FL involves a frequent exchange of…

Existing distributed machine learning (DML) systems focus on improving the computational efficiency of distributed learning, whereas communication aspects have received less attention. Many DML systems treat the network as a blackbox. Thus,…

Network Functions Virtualization, or NFV, enables telecommunications infrastructure providers to replace special-purpose networking equipment with commodity servers running virtualized network functions (VNFs). A service provider utilizing…

Virtualization provides an abstraction layer for the Internet of Things technology to tackle the heterogeneity of the edge networks. It enables the deployment of an application on devices with different architectures to achieve uniformity.…

The algorithm-to-hardware High-level synthesis (HLS) tools today are purported to produce hardware comparable in quality to handcrafted designs, particularly with user directive driven or domains specific HLS. However, HLS tools are not…

Decentralized federated learning (DFL) uses peer-to-peer communication to avoid the single point of failure problem in federated learning and has been considered an attractive solution for machine learning tasks on distributed devices. We…

We present N2Net, a system that implements binary neural networks using commodity switching chips deployed in network switches and routers. Our system shows that these devices can run simple neural network models, whose input is encoded in…

Deep neural networks (DNN) have shown superior performance in a variety of tasks. As they rapidly evolve, their escalating computation and memory demands make it challenging to deploy them on resource-constrained edge devices. Though…

In the present-day, distributed applications are commonly spread across multiple datacenters, reaching out to edge and fog computing locations. The transition away from single datacenter hosting is driven by capacity constraints in…

Wireless networks with multiple nodes that relay information from a source to a destination are expected to be deployed in many applications. Therefore, understanding their design and performance under practical constraints is important. In…

The rapid growth of Internet of Things (IoT) devices has generated vast amounts of data, leading to the emergence of federated learning as a novel distributed machine learning paradigm. Federated learning enables model training at the edge,…

Latency and energy consumption are key metrics in the performance of deep neural network (DNN) accelerators. A significant factor contributing to latency and energy is data transfers. One method to reduce transfers or data is reusing data…