1,866 research outputs found
Extending the performance of hybrid NoCs beyond the limitations of network heterogeneity
To meet the performance and scalability demands of the fast-paced technological growth towards exascale and Big-Data processing with the performance bottleneck of conventional metal based interconnects (wireline), alternative interconnect fabrics such as inhomogeneous three-dimensional integrated Network-on-Chip (3D NoC) and hybrid wired-wireless Network-on-Chip (WiNoC) have emanated as a cost-effective solution for emerging System-on-Chip (SoC) design. However, these interconnects trade-off optimized performance for cost by restricting the number of area and power hungry 3D routers and wireless nodes. Moreover, the non-uniform distributed traffic in chip multiprocessor (CMP) demands an on-chip communication infrastructure which can avoid congestion under high traffic conditions while possessing minimal pipeline delay at low-load conditions. To this end, in this paper, we propose a low-latency adaptive router with a low-complexity single-cycle bypassing mechanism to alleviate the performance degradation due to the slow 2D routers in such emerging hybrid NoCs. The proposed router transmits a flit using dimension-ordered routing (DoR) in the bypass datapath at low-loads. When the output port required for intra-dimension bypassing is not available, the packet is routed adaptively to avoid congestion. The router also has a simplified virtual channel allocation (VA) scheme that yields a non-speculative low-latency pipeline. By combining the low-complexity bypassing technique with adaptive routing, the proposed router is able balance the traffic in hybrid NoCs to achieve low-latency communication under various traffic loads. Simulation shows that, the proposed router can reduce applications’ execution time by an average of 16.9% compared to low-latency routers such as SWIFT. By reducing the latency between 2D routers (or wired nodes) and 3D routers (or wireless nodes) the proposed router can improve performance efficiency in terms of average packet delay by an average of 45% (or 50%) in 3D NoCs (or WiNoCs)
AI/ML Algorithms and Applications in VLSI Design and Technology
An evident challenge ahead for the integrated circuit (IC) industry in the
nanometer regime is the investigation and development of methods that can
reduce the design complexity ensuing from growing process variations and
curtail the turnaround time of chip manufacturing. Conventional methodologies
employed for such tasks are largely manual; thus, time-consuming and
resource-intensive. In contrast, the unique learning strategies of artificial
intelligence (AI) provide numerous exciting automated approaches for handling
complex and data-intensive tasks in very-large-scale integration (VLSI) design
and testing. Employing AI and machine learning (ML) algorithms in VLSI design
and manufacturing reduces the time and effort for understanding and processing
the data within and across different abstraction levels via automated learning
algorithms. It, in turn, improves the IC yield and reduces the manufacturing
turnaround time. This paper thoroughly reviews the AI/ML automated approaches
introduced in the past towards VLSI design and manufacturing. Moreover, we
discuss the scope of AI/ML applications in the future at various abstraction
levels to revolutionize the field of VLSI design, aiming for high-speed, highly
intelligent, and efficient implementations
A survey on scheduling and mapping techniques in 3D Network-on-chip
Network-on-Chips (NoCs) have been widely employed in the design of
multiprocessor system-on-chips (MPSoCs) as a scalable communication solution.
NoCs enable communications between on-chip Intellectual Property (IP) cores and
allow those cores to achieve higher performance by outsourcing their
communication tasks. Mapping and Scheduling methodologies are key elements in
assigning application tasks, allocating the tasks to the IPs, and organising
communication among them to achieve some specified objectives. The goal of this
paper is to present a detailed state-of-the-art of research in the field of
mapping and scheduling of applications on 3D NoC, classifying the works based
on several dimensions and giving some potential research directions
Cross-layer modeling and optimization of next-generation internet networks
Scaling traditional telecommunication networks so that they are able to cope with the volume of future traffic demands and the stringent European Commission (EC) regulations on emissions would entail unaffordable investments. For this very reason, the design of an innovative ultra-high bandwidth power-efficient network architecture is nowadays a bold topic within the research community. So far, the independent evolution of network layers has resulted in isolated, and hence, far-from-optimal contributions, which have eventually led to the issues today's networks are facing such as inefficient energy strategy, limited network scalability and flexibility, reduced network manageability and increased overall network and customer services costs. Consequently, there is currently large consensus among network operators and the research community that cross-layer interaction and coordination is fundamental for the proper architectural design of next-generation Internet networks.
This thesis actively contributes to the this goal by addressing the modeling, optimization and performance analysis of a set of potential technologies to be deployed in future cross-layer network architectures. By applying a transversal design approach (i.e., joint consideration of several network layers), we aim for achieving the maximization of the integration of the different network layers involved in each specific problem. To this end, Part I provides a comprehensive evaluation of optical transport networks (OTNs) based on layer 2 (L2) sub-wavelength switching (SWS) technologies, also taking into consideration the impact of physical layer impairments (PLIs) (L0 phenomena). Indeed, the recent and relevant advances in optical technologies have dramatically increased the impact that PLIs have on the optical signal quality, particularly in the context of SWS networks. Then, in Part II of the thesis, we present a set of case studies where it is shown that the application of operations research (OR) methodologies in the desing/planning stage of future cross-layer Internet network architectures leads to the successful joint optimization of key network performance indicators (KPIs) such as cost (i.e., CAPEX/OPEX), resources usage and energy consumption. OR can definitely play an important role by allowing network designers/architects to obtain good near-optimal solutions to real-sized problems within practical running times
Doctor of Philosophy
dissertationPortable electronic devices will be limited to available energy of existing battery chemistries for the foreseeable future. However, system-on-chips (SoCs) used in these devices are under a demand to offer more functionality and increased battery life. A difficult problem in SoC design is providing energy-efficient communication between its components while maintaining the required performance. This dissertation introduces a novel energy-efficient network-on-chip (NoC) communication architecture. A NoC is used within complex SoCs due it its superior performance, energy usage, modularity, and scalability over traditional bus and point-to-point methods of connecting SoC components. This is the first academic research that combines asynchronous NoC circuits, a focus on energy-efficient design, and a software framework to customize a NoC for a particular SoC. Its key contribution is demonstrating that a simple, asynchronous NoC concept is a good match for low-power devices, and is a fruitful area for additional investigation. The proposed NoC is energy-efficient in several ways: simple switch and arbitration logic, low port radix, latch-based router buffering, a topology with the minimum number of 3-port routers, and the asynchronous advantages of zero dynamic power consumption while idle and the lack of a clock tree. The tool framework developed for this work uses novel methods to optimize the topology and router oorplan based on simulated annealing and force-directed movement. It studies link pipelining techniques that yield improved throughput in an energy-efficient manner. A simulator is automatically generated for each customized NoC, and its traffic generators use a self-similar message distribution, as opposed to Poisson, to better match application behavior. Compared to a conventional synchronous NoC, this design is superior by achieving comparable message latency with half the energy
A critical analysis of research potential, challenges and future directives in industrial wireless sensor networks
In recent years, Industrial Wireless Sensor Networks (IWSNs) have emerged as an important research theme with applications spanning a wide range of industries including automation, monitoring, process control, feedback systems and automotive. Wide scope of IWSNs applications ranging from small production units, large oil and gas industries to nuclear fission control, enables a fast-paced research in this field. Though IWSNs offer advantages of low cost, flexibility, scalability, self-healing, easy deployment and reformation, yet they pose certain limitations on available potential and introduce challenges on multiple fronts due to their susceptibility to highly complex and uncertain industrial environments. In this paper a detailed discussion on design objectives, challenges and solutions, for IWSNs, are presented. A careful evaluation of industrial systems, deadlines and possible hazards in industrial atmosphere are discussed. The paper also presents a thorough review of the existing standards and industrial protocols and gives a critical evaluation of potential of these standards and protocols along with a detailed discussion on available hardware platforms, specific industrial energy harvesting techniques and their capabilities. The paper lists main service providers for IWSNs solutions and gives insight of future trends and research gaps in the field of IWSNs
Network-on-Chip
Addresses the Challenges Associated with System-on-Chip Integration Network-on-Chip: The Next Generation of System-on-Chip Integration examines the current issues restricting chip-on-chip communication efficiency, and explores Network-on-chip (NoC), a promising alternative that equips designers with the capability to produce a scalable, reusable, and high-performance communication backbone by allowing for the integration of a large number of cores on a single system-on-chip (SoC). This book provides a basic overview of topics associated with NoC-based design: communication infrastructure design, communication methodology, evaluation framework, and mapping of applications onto NoC. It details the design and evaluation of different proposed NoC structures, low-power techniques, signal integrity and reliability issues, application mapping, testing, and future trends. Utilizing examples of chips that have been implemented in industry and academia, this text presents the full architectural design of components verified through implementation in industrial CAD tools. It describes NoC research and developments, incorporates theoretical proofs strengthening the analysis procedures, and includes algorithms used in NoC design and synthesis. In addition, it considers other upcoming NoC issues, such as low-power NoC design, signal integrity issues, NoC testing, reconfiguration, synthesis, and 3-D NoC design. This text comprises 12 chapters and covers: The evolution of NoC from SoC—its research and developmental challenges NoC protocols, elaborating flow control, available network topologies, routing mechanisms, fault tolerance, quality-of-service support, and the design of network interfaces The router design strategies followed in NoCs The evaluation mechanism of NoC architectures The application mapping strategies followed in NoCs Low-power design techniques specifically followed in NoCs The signal integrity and reliability issues of NoC The details of NoC testing strategies reported so far The problem of synthesizing application-specific NoCs Reconfigurable NoC design issues Direction of future research and development in the field of NoC Network-on-Chip: The Next Generation of System-on-Chip Integration covers the basic topics, technology, and future trends relevant to NoC-based design, and can be used by engineers, students, and researchers and other industry professionals interested in computer architecture, embedded systems, and parallel/distributed systems
EVA: Emergency Vehicle Allocation
Emergency medicine plays a critical role in the development of a community, where the goal is
to provide medical assistance in the shortest possible time. Consequently, the systems that
support emergency operations need to be robust, efficient, and effective when managing the
limited resources at their disposal. To achieve this, operators analyse historical data in search
of patterns present in past occurrencesthat could help predict future call volume. This is a time consuming and very complex task that could be solved by the usage of machine learning
solutions, which have been performed appropriately in the context of time series forecasting.
Only after the future demands are known, the optimization of the distribution of available
assets can be done, for the purpose of supporting high-density zones. The current works aim to
propose an integrated system capable of supporting decision-making emergency operations in
a real-time environment by allocating a set of available units within a service area based on
hourly call volume predictions. The suggested system architecture employs a microservices
approach along with event-based communications to enable real-time interactions between
every component. This dissertation focuses on call volume forecasting and optimizing allocation
components. A combination of traditional time series and deep learning models was used to
model historical data from Virginal Beach emergency calls between the years 2010 and 2018,
combined with several other features such as weather-related information. Deep learning
solutions offered better error metrics, with WaveNet having an MAE value of 0.04. Regarding
optimizing emergency vehicle location, the proposed solution is based on a Linear Programming
problem to minimize the number of vehicles in each station, with a neighbour mechanism,
entitled EVALP-NM, to add a buffer to stations near a high-density zone. This solution was also
compared against a Genetic Algorithm that performed significantly worse in terms of execution
time and outcomes. The performance of EVALP-NM was tested against simulations with
different settings like the number of zones, stations, and ambulances.A medicina de emergência desempenha um papel fundamental no desenvolvimento da
Sociedade, onde o objetivo é prestar assistência médica no menor tempo possível.
Consequentemente, os sistemas que apoiam as operações de emergência precisam de ser
robustos, eficientes e eficazes na gestão dos recursos limitados. Para isso, são analisados dados
históricos no intuito de encontrar padrões em ocorrências passadas que possam ajudar a prever
o volume futuro de chamadas. Esta é uma tarefa demorada e muito complexa que poderia ser
resolvida com o uso de soluções de Machine Learning, que têm funcionado adequadamente no
contexto da previsão de séries temporais. Só depois de conhecida a demanda futura poderá ser
feita a otimização da distribuição dos recursos disponíveis, com o objetivo de suportar zonas de
elevada densidade populacional. O presente trabalho tem como objetivo propor um sistema
integrado capaz de apoiar a tomada de decisão em operações de emergência num ambiente de
tempo real, atribuindo um conjunto de unidades disponíveis dentro de uma área de serviço
com base em previsões volume de chamadas a cada hora. A arquitetura de sistema sugerida
emprega uma abordagem de microserviços juntamente com comunicações baseadas em
eventos para permitir interações em tempo real entre os componentes. Esta dissertação centra se nos componentes de previsão do volume de chamadas e otimização da atribuição. Foram
usados modelos de séries temporais tradicionais e Deep Learning para modelar dados históricos
de chamadas de emergência de Virginal Beach entre os anos de 2010 e 2018, combinadas com
informações relacionadas ao clima. As soluções de Deep Learning ofereceram melhores
métricas de erro, com WaveNet a ter um valor MAE de 0,04. No que diz respeito à otimização
da localização dos veículos de emergência, a solução proposta baseia-se num problema de
Programação Linear para minimizar o número de veículos em cada estação, com um mecanismo
de vizinho, denominado EVALP-NM, para adicionar unidades adicionais às estações próximas
de uma zona de alta densidade de chamadas. Esta solução foi comparada com um algoritmo
genético que teve um desempenho significativamente pior em termos de tempo de execução
e resultados. O desempenho do EVALP-NM foi testado em simulações com configurações
diferentes, como número de zonas, estações e ambulâncias
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