VIRTUAL MEMORY ON A MANY-CORE NOC

Many-core devices are likely to become increasingly common in real-time and embedded systems as computational demands grow and as expectations for higher performance can generally only be met by by increasing core numbers rather than relying on higher clock speeds. Network-on-chip devices, where multiple cores share a single slice of silicon and employ packetised communications, are a widely-deployed many-core option for system designers. As NoCs are expected to run larger and more complex programs, the small amount of fast, on-chip memory available to each core is unlikely to be sufficient for all but the simplest of tasks, and it is necessary to find an efficient, effective, and time-bounded, means of accessing resources stored in off-chip memory, such as DRAM or Flash storage. The abstraction of paged virtual memory is a familiar technique to manage similar tasks in general computing but has often been shunned by real-time developers because of concern about time predictability. We show it can be a poor choice for a many-core NoC system as, unmodified, it typically uses page sizes optimised for interaction with spinning disks and not solid state media, and transports significant volumes of subsequently unused data across already congested links. In this work we outline and simulate an efficient partial paging algorithm where only those memory resources that are locally accessed are transported between global and local storage. We further show that smaller page sizes add to efficiency. We examine the factors that lead to timing delays in such systems, and show we can predict worst case execution times at even safety-critical thresholds by using statistical methods from extreme value theory. We also show these results are applicable to systems with a variety of connections to memory

Energy-efficient wireless communication

In this chapter we present an energy-efficient highly adaptive network interface architecture and a novel data link layer protocol for wireless networks that provides Quality of Service (QoS) support for diverse traffic types. Due to the dynamic nature of wireless networks, adaptations in bandwidth scheduling and error control are necessary to achieve energy efficiency and an acceptable quality of service. In our approach we apply adaptability through all layers of the protocol stack, and provide feedback to the applications. In this way the applications can adapt the data streams, and the network protocols can adapt the communication parameters

Towards Massive Machine Type Communications in Ultra-Dense Cellular IoT Networks: Current Issues and Machine Learning-Assisted Solutions

The ever-increasing number of resource-constrained Machine-Type Communication (MTC) devices is leading to the critical challenge of fulfilling diverse communication requirements in dynamic and ultra-dense wireless environments. Among different application scenarios that the upcoming 5G and beyond cellular networks are expected to support, such as eMBB, mMTC and URLLC, mMTC brings the unique technical challenge of supporting a huge number of MTC devices, which is the main focus of this paper. The related challenges include QoS provisioning, handling highly dynamic and sporadic MTC traffic, huge signalling overhead and Radio Access Network (RAN) congestion. In this regard, this paper aims to identify and analyze the involved technical issues, to review recent advances, to highlight potential solutions and to propose new research directions. First, starting with an overview of mMTC features and QoS provisioning issues, we present the key enablers for mMTC in cellular networks. Along with the highlights on the inefficiency of the legacy Random Access (RA) procedure in the mMTC scenario, we then present the key features and channel access mechanisms in the emerging cellular IoT standards, namely, LTE-M and NB-IoT. Subsequently, we present a framework for the performance analysis of transmission scheduling with the QoS support along with the issues involved in short data packet transmission. Next, we provide a detailed overview of the existing and emerging solutions towards addressing RAN congestion problem, and then identify potential advantages, challenges and use cases for the applications of emerging Machine Learning (ML) techniques in ultra-dense cellular networks. Out of several ML techniques, we focus on the application of low-complexity Q-learning approach in the mMTC scenarios. Finally, we discuss some open research challenges and promising future research directions.Comment: 37 pages, 8 figures, 7 tables, submitted for a possible future publication in IEEE Communications Surveys and Tutorial