Performance Evaluation for Hybrid Architectures

In this dissertation we discuss methologies for estimating the performance of applications on hybrid architectures, systems that include various types of computing resources (e.g. traditional general-purpose processors, chip multiprocessors, reconfigurable hardware). A common use of hybrid architectures will be to deploy coarse pipeline stages of application on suitable compute units with communication path for transferring data. The first problem we focus on relates to the sizing the data queues between the different processing elements of an hybrid system. Much of the discussion centers on our analytical models that can be used to derive performance metrics of interest such as, throughput and stalling probability for networks of processing elements with finite data buffering between them. We then discuss to the reliability of performance models. There we start by presenting scenarios where our analytical model is reliable, and introduce tests that can detect their inapplicability. As we transition into the question of reliability of performance models, we access the accuracy and applicability of various evaluation methods. We present results from our experiments to show the need for measuring and accounting for operating system effects in architectural modeling and estimation

Analytical models of a fault-tolerant multiple module microprocessor system

Imperial Users onl

Dependable Embedded Systems

This Open Access book introduces readers to many new techniques for enhancing and optimizing reliability in embedded systems, which have emerged particularly within the last five years. This book introduces the most prominent reliability concerns from today’s points of view and roughly recapitulates the progress in the community so far. Unlike other books that focus on a single abstraction level such circuit level or system level alone, the focus of this book is to deal with the different reliability challenges across different levels starting from the physical level all the way to the system level (cross-layer approaches). The book aims at demonstrating how new hardware/software co-design solution can be proposed to ef-fectively mitigate reliability degradation such as transistor aging, processor variation, temperature effects, soft errors, etc. Provides readers with latest insights into novel, cross-layer methods and models with respect to dependability of embedded systems; Describes cross-layer approaches that can leverage reliability through techniques that are pro-actively designed with respect to techniques at other layers; Explains run-time adaptation and concepts/means of self-organization, in order to achieve error resiliency in complex, future many core systems

Machine Learning for Architectural Design Space Exploration and Resource Control

Machine learning has enabled significant advancements in diverse fields, yet, with a few exceptions, has had limited impact on computer architecture. Recent work, however, has begun to explore broader application to design, optimization, and simulation. Notably, machine-learning-based strategies often surpass prior state-of-the-art analytical, heuristic, and human-expert approaches. This thesis first reviews existing work applying machine learning to architecture, ranging from simulation and run-time optimization, to individual component design involving the memory system, branch predictors, networks-on-chip, and GPUs. Next, the thesis presents a novel deep-reinforcement-learning framework for design space exploration. Finally, the thesis introduces an innovative strategy for resource optimization with multiple co-scheduled workloads. Taken together, these works present a promising future for machine-learning-based architectural design

On the efficiency of dynamic licensed shared access for 5G/6G wireless communications

The licensed shared access (LSA) is a spectrum licensing scheme authorizing additional new users (the licensees) to dynamically share the same spectrum with the old users (the incumbents). Contained in the terms of the spectrum usage authorization is a set of strict protective measures for the incumbent system which introduce extra restrictions on the licensee operations. Such measures imply that the licensee’s access to the spectrum can be revoked or restricted at any time which may result in the degradation of critical performance metrics of the latter. Addressing this issue and the accompanying challenges as we enter the 5G zettabytes era motivates the research problems addressed in this thesis. A vertical LSA spectrum sharing involving a mobile network operator (MNO) as the licensee and two categories of incumbent including the aeronautical telemetry, and a group of terrestrial public and ancillary wireless services is adopted in this thesis. Firstly, an analytical examination of the uplink and downlink licensee’s transmit power, when its spectrum access right is revoked (i.e., the limited transmit power) is done. Then a power allocation scheme that maximizes the energy efficiency (EE) of the licensee when it is operating with limited transmit power is proposed. Simulation results reveal the impact of the LSA spectrum access revocation on the allowable transmit power of the licensee as a function of the effect of different interference propagation path and the transmission direction. A comparison of the proposed optimal power allocation method with the equal power allocation (EPA) method further shows considerable improvement in the achievable EE of the licensee. Furthermore, in the LSA, the achievable spectrum efficiency (SE) of the licensee is limited by the interference threshold constraint set by the incumbent’s protective measures. Consequent on this, we propose an SE maximization of the licensee’s system subject to the incumbent interference threshold constraint. Furthermore, the LSA band spectral utilization was characterised as a function of the licensee’s achievable SE and the statistics of the LSA spectrum availability. The obtained results provide quantitative insights for practical system design and deployment of the LSA system, especially when compared to the results obtained in the maximization of the EE. In particular, the effect of variations in critical operational parameters throws up interesting network design trade-off challenge, worthy of consideration. This informs the subsequent multi objective optimization of the EE-SE trade-off investigated next. Interestingly, the obtained results indicate that with careful selection of the licensee eNodeB coverage radius, transmit power, and number of user equipment per eNodeB coverage area, one can engineer the best possible trade-off between the spectrum and energy efficiency in practical LSA deployment. A major LSA feature is guaranteeing predictable quality of service (QoS) for both the incumbent and the licensee systems. In terrestrial implementation, the reduction in the achievable data rate caused by the incumbents’ protective measures, may violate guaranteed QoS in the licensee system. To address this issue, we propose a LSA - based hybrid aerialterrestrial system with drone base station (D-BS). Simulation results show that using the proposed scheme, the licensee, when operating under the incumbents’ imposed restrictions, is able to achieve the QoS data rate requirements of the users on its network. In conclusion, the findings in this research indicates that the dynamic LSA is a practically viable solution to the spectrum management requirements of the emerging vertical wireless technologies in 5G and beyond

Quantum Machine Learning for 6G Communication Networks: State-of-the-Art and Vision for the Future

The upcoming 5th Generation (5G) of wireless networks is expected to lay a foundation of intelligent networks with the provision of some isolated Artificial Intelligence (AI) operations. However, fully-intelligent network orchestration and management for providing innovative services will only be realized in Beyond 5G (B5G) networks. To this end, we envisage that the 6th Generation (6G) of wireless networks will be driven by on-demand self-reconfiguration to ensure a many-fold increase in the network performanceandservicetypes.Theincreasinglystringentperformancerequirementsofemergingnetworks may finally trigger the deployment of some interesting new technologies such as large intelligent surfaces, electromagnetic-orbital angular momentum, visible light communications and cell-free communications – tonameafew.Ourvisionfor6Gis–amassivelyconnectedcomplexnetworkcapableofrapidlyresponding to the users’ service calls through real-time learning of the network state as described by the network-edge (e.g., base-station locations, cache contents, etc.), air interface (e.g., radio spectrum, propagation channel, etc.), and the user-side (e.g., battery-life, locations, etc.). The multi-state, multi-dimensional nature of the network state, requiring real-time knowledge, can be viewed as a quantum uncertainty problem. In this regard, the emerging paradigms of Machine Learning (ML), Quantum Computing (QC), and Quantum ML (QML) and their synergies with communication networks can be considered as core 6G enablers. Considering these potentials, starting with the 5G target services and enabling technologies, we provide a comprehensivereviewoftherelatedstate-of-the-artinthedomainsofML(includingdeeplearning),QCand QML, and identify their potential benefits, issues and use cases for their applications in the B5G networks. Subsequently,weproposeanovelQC-assistedandQML-basedframeworkfor6Gcommunicationnetworks whilearticulatingitschallengesandpotentialenablingtechnologiesatthenetwork-infrastructure,networkedge, air interface and user-end. Finally, some promising future research directions for the quantum- and QML-assisted B5G networks are identified and discussed

Flexible Automation and Intelligent Manufacturing: The Human-Data-Technology Nexus

This is an open access book. It gathers the first volume of the proceedings of the 31st edition of the International Conference on Flexible Automation and Intelligent Manufacturing, FAIM 2022, held on June 19 – 23, 2022, in Detroit, Michigan, USA. Covering four thematic areas including Manufacturing Processes, Machine Tools, Manufacturing Systems, and Enabling Technologies, it reports on advanced manufacturing processes, and innovative materials for 3D printing, applications of machine learning, artificial intelligence and mixed reality in various production sectors, as well as important issues in human-robot collaboration, including methods for improving safety. Contributions also cover strategies to improve quality control, supply chain management and training in the manufacturing industry, and methods supporting circular supply chain and sustainable manufacturing. All in all, this book provides academicians, engineers and professionals with extensive information on both scientific and industrial advances in the converging fields of manufacturing, production, and automation

Scheduling in wireless cellular data networks

This thesis studies the performance of scheduling policies in a wireless cellular data network. We consider a cell within the network. The cell has a single base station serving a given number of users in the cell. Time is slotted and the base station can serve at most one user in a given time slot. The users are mobile and therefore the data transfer rate available to each user changes from time slot to time slot depending on the distance from the base station and the terrain of the user. There are two conflicting objectives for the base station: maximize the data throughput per time slot, and maintaining fairness'. To maximize the data throughput, the base station would like to serve the user with the highest available data rate, but this can lead to starvation of some users. To ensure fairness, no user should be unserved for a long' time, i.e., users should be served in a round-robin manner. Although this problem has been studied in the literature to some extent, existing methods to do this are ad-hoc. Our goal is to derive policies that have a sound theoretical basis, and at the same time are computationally tractable, are easy to implement, are fair to all the users and beneficial for the service providers. We formulate the problem of finding an optimal scheduling policy as a Markov Decision Process (MDP) and prove some characteristics of the optimal policy. Since solving the MDP to optimality is infeasible, given the huge size of the problem, we develop heuristic policies called index policies'. These policies are based on a closed form index' for every user that depends only its own current state. We derive this index using a policy improvement approach based on Markov Decision Processes. We also compare their performance with existing policies through simulation. We develop such index policies in two settings: when every user always has ample data waiting for it to be served (the infinitely backlogged case), and when data arrives for every user in every time slot according to some distribution (the external data arrival case). Further, we consider the case of users entering and leaving the cell as well, but only from a simulation perspective