Related papers: TEGRA: A Flexible & Scalable NextGen Mobile Core

Multi-tenancy allows diverse agents sharing the infrastructure in the 5 th generation of mobile networks. Such a feature calls for more automated and faster planning procedures in order to adapt the network capacity to the varying traffic…

Deep Learning Recommendation Models (DLRMs) play a crucial role in delivering personalized content across web applications such as social networking and video streaming. However, with improvements in performance, the parameter size of DLRMs…

The emergence of 5G technology marks a significant milestone in developing telecommunication networks, enabling exciting new applications such as augmented reality and self-driving vehicles. However, these improvements bring an increased…

The Fifth Generation (5G) of mobile networks offers new and advanced services with stricter requirements. Multi-access Edge Computing (MEC) is a key technology that enables these new services by deploying multiple devices with computing and…

The integration of Non-Terrestrial Networks (NTN) with Terrestrial Networks (TN) is a key enabler for resilient 5G-Advanced and future 6G backhaul infrastructures. However, managing traffic across these highly asymmetric links remains a…

The year of 2020 has witnessed the unprecedented development of 5G networks, along with the widespread deployment of 5G base stations (BSs). Nevertheless, the enormous energy consumption of BSs and the incurred huge energy cost have become…

Mobile edge computing is a provisioning solution to enable Augmented Reality (AR) applications on mobile devices. AR mobile applications have inherent collaborative properties in terms of data collection in the uplink, computing at the…

FPGAs are increasingly being deployed in the cloud to accelerate diverse applications. They are to be shared among multiple tenants to improve the total cost of ownership. Partial reconfiguration technology enables multi-tenancy on FPGA by…

Spin-Transfer Torque RAMs (STTRAMs) have been shown to offer much promise for implementing emerging cache architectures. This paper studies the viability of STTRAM caches for mobile workloads from the perspective of energy and latency.…

The autoregressive decoding in LLMs is the major inference bottleneck due to the memory-intensive operations and limited hardware bandwidth. 3D-stacked architecture is a promising solution with significantly improved memory bandwidth, which…

Fifth generation (5G) new radio introduced flexible numerology to provide the necessary flexibility for accommodating heterogeneous services. However, optimizing the scheduling of heterogeneous services with differing delay and throughput…

The evolution of MobileNets has laid a solid foundation for neural network applications on mobile end. With the latest MobileNetV3, neural architecture search again claimed its supremacy in network design. Unfortunately, till today all…

The rapid growth in the number and variety of connected devices requires 5G wireless systems to cope with a very heterogeneous traffic mix. As a consequence, the use of a fixed TTI during transmission is not necessarily the most efficacious…

Mobile cloud gaming enables high-end games on constrained devices by streaming the game content from powerful servers through mobile networks. Mobile networks suffer from highly variable bandwidth, latency, and losses that affect the gaming…

Autonomous driving platforms encounter diverse driving scenarios, each with varying hardware resources and precision requirements. Given the computational limitations of embedded devices, it is crucial to consider computing costs when…

This work presents a perspective on addressing the underutilization of computing resources in FPGA SoC devices deployed in 5G radio and edge computing infrastructure. The initial step in this approach involves developing a resource…

The ongoing research and industrial exploitation of SDN and NFV technologies promise higher flexibility on network automation and infrastructure optimization. Choosing the location of Virtual Network Functions is a central problem in the…

In this article two new algorithms are presented that convert a given data tensor train into either a Tucker decomposition with orthogonal matrix factors or a multi-scale entanglement renormalization ansatz (MERA). The Tucker core tensor is…

The emergence of heterogeneity and domain-specific architectures targeting deep learning inference show great potential for enabling the deployment of modern CNNs on resource-constrained embedded platforms. A significant development is the…

Driven by the emergence of new compute-intensive applications and the vision of the Internet of Things (IoT), it is foreseen that the emerging 5G network will face an unprecedented increase in traffic volume and computation demands.…