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Differentiable ARchiTecture Search (DARTS) is one of the most trending Neural Architecture Search (NAS) methods. It drastically reduces search cost by resorting to weight-sharing. However, it also dramatically reduces the search space, thus…

Network distance (Round Trip Time, RTT) is an important parameter for many Internet distributed systems to optimize their performances. Network Coordinate System (NCS) is assumed as a lightweight and scalable mechanism to predict network…

Partitioned DNN inference is a promising approach for latency-sensitive intelligent services in edge networks, since it allows different parts of a model to be executed across end devices, edge servers, and the cloud. However, in a…

Deep neural networks (DNN) are increasingly being accelerated on application-specific hardware such as the Google TPU designed especially for deep learning. Timing speculation is a promising approach to further increase the energy…

Popularly used to distribute a variety of multimedia content items in today Internet, HTTP-based web content delivery still suffers from various content delivery failures. Hindered by the expensive deployment cost, the conventional CDN can…

Information-Centric Networking (ICN) has recently emerged as a prominent candidate for the Future Internet Architecture (FIA) that addresses existing issues with the host-centric communication model of the current TCP/IP-based Internet.…

The purpose of network representation is to learn a set of latent features by obtaining community information from network structures to provide knowledge for machine learning tasks. Recent research has driven significant progress in…

Power delivery network (PDN) design is a nontrivial, time-intensive, and iterative task. Correct PDN design must account for considerations related to power bumps, currents, blockages, and signal congestion distribution patterns. This work…

Content Delivery Networks (CDNs) are becoming an integral part of the future generation Internet. Traditionally, these networks have been designed with the goals of traffic offload and the improvement of users' quality of experience (QoE),…

With the rapid growth of artificial intelligence (AI) workloads in datacenters, the Ultra Ethernet Consortium (UEC) has defined a new high-performance transport layer to deliver the required performance at scale. A core component of this…

This paper optimizes the scheduling and routing of the co-flows of MapReduce shuffling phase in state-of-the-art and proposed Passive Optical Networking (PON)-based Data Centre Network (DCN) architectures. A time-slotted Mixed Integer…

Designing lightweight convolutional neural network (CNN) models is an active research area in edge AI. Compute-in-memory (CIM) provides a new computing paradigm to alleviate time and energy consumption caused by data transfer in von Neumann…

The current strong divide between applications and the network control plane is desirable for many reasons; but a downside is that the network is kept in the dark regarding the ultimate purposes and intentions of applications and, as a…

Cross-domain recommendation (CDR) has been proven as a promising way to tackle the user cold-start problem, which aims to make recommendations for users in the target domain by transferring the user preference derived from the source…

Computation and Data Reuse is critical for the resource-limited Convolutional Neural Network (CNN) accelerators. This paper presents Universal Computation Reuse to exploit weight sparsity, repetition, and similarity simultaneously in a…

An emerging trend of next generation communication systems is to provide network edges with additional capabilities such as additional storage resources in the form of caches to reduce file delivery latency. To investigate this aspect, we…

Many convolutional neural network (CNN) accelerators face performance- and energy-efficiency challenges which are crucial for embedded implementations, due to high DRAM access latency and energy. Recently, some DRAM architectures have been…

Information-Centric Networking (ICN) is a new networking paradigm, which replaces the widely used host-centric networking paradigm in communication networks (e.g., Internet, mobile ad hoc networks) with an information-centric paradigm,…

The emerging paradigm of Information-Centric Networking is an exciting field of research, opening opportunities in many areas, such as forwarding strategies, caching placement policies, applications (e.g. video streaming and instant…

Routing in NDN networks must scale in terms of forwarding table size and routing protocol overhead. Hyperbolic routing (HR) presents a potential solution to address the routing scalability problem, because it does not use traditional…