Energy-Efficient Digital Circuit Design using Threshold Logic Gates

abstract: Improving energy efficiency has always been the prime objective of the custom and automated digital circuit design techniques. As a result, a multitude of methods to reduce power without sacrificing performance have been proposed. However, as the field of design automation has matured over the last few decades, there have been no new automated design techniques, that can provide considerable improvements in circuit power, leakage and area. Although emerging nano-devices are expected to replace the existing MOSFET devices, they are far from being as mature as semiconductor devices and their full potential and promises are many years away from being practical. The research described in this dissertation consists of four main parts. First is a new circuit architecture of a differential threshold logic flipflop called PNAND. The PNAND gate is an edge-triggered multi-input sequential cell whose next state function is a threshold function of its inputs. Second a new approach, called hybridization, that replaces flipflops and parts of their logic cones with PNAND cells is described. The resulting \hybrid circuit, which consists of conventional logic cells and PNANDs, is shown to have significantly less power consumption, smaller area, less standby power and less power variation. Third, a new architecture of a field programmable array, called field programmable threshold logic array (FPTLA), in which the standard lookup table (LUT) is replaced by a PNAND is described. The FPTLA is shown to have as much as 50% lower energy-delay product compared to conventional FPGA using well known FPGA modeling tool called VPR. Fourth, a novel clock skewing technique that makes use of the completion detection feature of the differential mode flipflops is described. This clock skewing method improves the area and power of the ASIC circuits by increasing slack on timing paths. An additional advantage of this method is the elimination of hold time violation on given short paths. Several circuit design methodologies such as retiming and asynchronous circuit design can use the proposed threshold logic gate effectively. Therefore, the use of threshold logic flipflops in conventional design methodologies opens new avenues of research towards more energy-efficient circuits.Dissertation/ThesisDoctoral Dissertation Computer Science 201

Contributions to network planning and operation of Flex-Grid/SDM optical core networks

Premi Extraordinari de Doctorat, promoció 2018-2019. Àmbit de les TICThe ever demanding bandwidth requirements for supporting emerging telecom services such as ultra-high-definition video streaming, cloud computing, connected car, virtual/augmented reality, etc., bring to the fore the necessity to upgrade continuously the technology behind transport networks in order to keep pace with this exponential traffic growth. Thus, everything seems to indicate that fixed-grid Wavelength-Division Multiplexed (WDM) networks will be upgraded by adopting a flexible-grid, thus providing finer bandwidth allocation granularities, and therefore, increasing the Grade-of-Service by packing more information in the same spectral band of standard Single-Mode Fibers (SMFs). Nevertheless, unfortunately, the fundamental Shannon’s limit of SMFs is rapidly approaching, and, then, the research efforts to increase the SMFs' capacity will be useless. One solution to overcome this capacity crunch effect is to enable one extra dimension in addition to the frequency one, namely, the spatial dimension, thus deploying S parallel paths in order to multiply, in the best case, by S the capacity of SMF-based networks. However, additionally, it is necessary to decrease the cost and energy per bit in order to provide economically attractive solutions. For this purpose, a smooth upgrade path has to be carried out as new integrated devices and system components are developed for Space Division Multiplexing (SDM). This thesis is concentrated on the planning and operation of the combined flexible WDM and SDM networks (i.e., Flex-Grid/SDM networks) proposing several strategies aimed at optimizing network resources usage with hardware complexity analysis. For this purpose, firstly, network problems are carefully studied and stated, and then, mathematical and/or heuristic algorithms are designed and implemented in an optical network simulator. Specifically, after an introduction to the thesis, chapter 2 presents the background and related work. Next, chapter 3 concentrates on the study of spatially fixed Flex-Grid/SDM networks, i.e., when a rigid number of spatial channels are reserved per allocated traffic demand. In its turn, chapter 4 studies the case of Spectrally-Spatially Flexible Optical Networks (SS-FONs), as the ones providing the upper-bound network capacity. Costs and hardware requirements implied on providing this flexibility are analyzed. Network nodes aimed at reducing the cost of SS-FONs are presented and evaluated in chapter 5. Finally, this thesis ends with the presentation of the main contributions and future research work in chapter 6.La demanda de ancho de banda cada vez más exigente para soportar servicios de telecomunicación emergentes tales como la transmisión de video de alta calidad, computación en la nube, vehículo conectado, realidad virtual/aumentada, etc.…, ha puesto de manifiesto la necesidad de actualizar constantemente la tecnología detrás de las redes de transporte óptico con la finalidad de ir a la par de este incremento exponencial del tráfico. De esta manera, todo parece indicar que las redes basadas en la multiplexación por division de longitud de onda (Wavelength Division Multiplexing, WDM) de ancho espectral fijo serán actualizadas adoptando un ancho de banda espectral flexible, que ofrece asignaciones de ancho de banda con granularidad más fina acorde a las demandas de tráfico; y por lo tanto, incremanta el Grado de Servicio de la red, ya que se permite acomodar mayor información en la misma banda espectral de las fibras monomodo (Single Mode Fibers, SMFs). Sin embargo, desafortunadamente, el límite de Shannon de las fibras monomodo se está aproximando cada vez más, y cuando esto ocurra las investigaciones para incrementar la capacidad de las fibras monomodo serán infructuosas. Una posible solución para superar este colapso de las fibras monomodo es habilitar la dimensión espacial a más de la frecuencial, desplegando � caminos paralelos con la finalidad de multiplicar por � (en el mejor de los casos) la capacidad de las fibras monomodo. No obstante, es necesario disminuir el costo y la energía por bit con la finalidad de proveer soluciones comerciales atractivas. Para tal propósito debe llevarse a cabo una actualización moderada conforme nuevos dispositivos y componentes integrados son desarrollados para la implementación de la tecnología basada en la multiplexación por división de espacio (Space Division Multiplexing, SDM). Esta tesis se concentra en la planificación y operación de la combinación de las redes WDM flexibles y SDM (es decir, de las redes Flex-Grid/SDM) proponiendo varias estrategias dirigidas a optimizar el uso de los recursos de red junto con el análisis de la complejidad del hardware que viene acompañada. Para este fin, primeramente, los problemas de red son cuidadosamente estudiados y descritos. A continuación, se han diseñado e implementado algoritmos basados en programación lineal entera o heurísticas en un simulador de redes ópticas. Después de una introducción inicial, el capítulo 2 de esta tesis presenta el marco teórico sobre los conceptos tratados y los trabajos publicados anteriormente. A continuación, el capítulo 3 se concentra en el estudio de las redes Flex-Grid/SDM con la dimensión espacial rígida; es decir, cuando un número fijo de canales espaciales son reservados por cada demanda de tráfico establecida. Por su parte, el capítulo 4 estudia las redes Flex-Grid/SDM considerando flexibilidad tanto en el dominio espacial como espectral (Spectrally and Spatially Flexible Optical Networks, SS-FONs), las cuales proveerían la capacidad máxima de las redes SDM. Adicionalmente, los costos y requerimientos de hardware implicados en la provisión de esta flexibilidad son analizados. El capítulo 5 presenta la evaluación de nodos orientados a reducir los costos de las SS-FONs. Finalmente, el capítulo 6 expone las principales contribuciones y las posibles líneas de trabajo futuroEls requisits incessants d’ample de banda per al suport de nous serveis de telecomunicació, com poden ser la difusió en directe de vídeo de molt alta definició, la informàtica en el núvol, els cotxes intel·ligents connectats a la xarxa, la realitat virtual/augmentada, etc…, han exigit una millora contínua de les tecnologíes de les actuals xarxes de transport de dades. Tot sembla indicar que les xarxes de transport òptiques actuals, basades en la tecnologia de multiplexació per divisió de longitud d’ona (Wavelength Division Multiplexing, WDM) sobre un grid espectral rígid, hauran de ser reemplaçades per tecnologies òptiques més flexibles, amb una granularitat més fina a l’hora de suportar noves connexions, incrementat el grau de servei de les xarxes gràcies a aprofitament major de l’ample de banda espectral proporcionat per les fibres òptiques monomode (Single Mode Fibers, SMFs). Tanmateix, estem exhaurint ja la capacitat màxima de les fibres òptiques SMF segons ens indica el límit fonamental de Shannon. Per tant, qualsevol esforç enfocat a millorar la capacitat d’aquestes xarxes basades en SMFs pot acabar sent infructuós. Una possible solució per superar aquestes limitacions de capacitat és explorar la dimensió espacial, a més de l’espectral, desplegant camins en paral·lel per tal de multiplicar per , en el millor cas, la capacitat de les SMFs. Tot i això, és necessari reduir el cost i el consum energètic per bit transmès, per tal de proporcionar solucions econòmicament viables. Amb aquest propòsit, pot ser necessària una migració progressiva, a mesura que es desenvolupen nous dispositius i components per aquesta nova tecnologia de multiplexació per divisió espacial (Spatial Division Multiplexing, SDM). La present tesi es centra en la planificació i operació de xarxes òptiques de nova generació que combinin tecnologies de xarxa WDM flexible i SDM (és a dir, xarxes Flex-Grid/SDM), proposant estratègies per a l’optimització de l’ús dels recursos de xarxa i, en definitiva, el seu cost (CapEx). Amb aquest propòsit, s’analitzen en primer moment els problemes adreçats. Tot seguit, es dissenyen algorismes per tal de solucionar-los, basats en tècniques de programació matemàtica i heurístiques, els quals s’implementen i es proven en un simulador de xarxa òptica. Després d’una introducció inicial, el capítol 2 d’aquesta tesi presenta tots els conceptes tractats i treballs relacionats publicats amb anterioritat. Tot seguit, el capítol 3 es centra en l’estudi de les xarxes Flex-Grid/SDM fixes en el domini espai, és a dir, on sempre es reserva un nombre rígid de canals espacials per qualsevol demanda suportada. El capítol 4 estudia les xarxes flexibles en els dominis espectrals i espacials (Spectrally-Spatially Flexible Optical Nextworks, SS-FONs), com aquelles que poden proporcionar una capacitat de xarxa màxima. En aquest context, s’analitzen els requeriments en termes de cost i hardware per tal de proporcionar aquesta flexibilitat. Llavors, en el capítol 6 es presenten opcions de node de xarxa capaces de reduir els costos de les xarxes SS-FONs. Finalment, en el capítol 7 es repassen totes les contribucions de la tesi, així com posibles línies de treball futurAward-winningPostprint (published version

Journal of Telecommunications and Information Technology, 2010, nr 3

kwartalni

Integrated machine learning and optimization approaches

This dissertation focuses on the integration of machine learning and optimization. Specifically, novel machine learning-based frameworks are proposed to help solve a broad range of well-known operations research problems to reduce the solution times. The first study presents a bidirectional Long Short-Term Memory framework to learn optimal solutions to sequential decision-making problems. Computational results show that the framework significantly reduces the solution time of benchmark capacitated lot-sizing problems without much loss in feasibility and optimality. Also, models trained using shorter planning horizons can successfully predict the optimal solution of the instances with longer planning horizons. For the hardest data set, the predictions at the 25% level reduce the solution time of 70 CPU hours to less than 2 CPU minutes with an optimality gap of 0.8% and without infeasibility. In the second study, an extendable prediction-optimization framework is presented for multi-stage decision-making problems to address the key issues of sequential dependence, infeasibility, and generalization. Specifically, an attention-based encoder-decoder neural network architecture is integrated with an infeasibility-elimination and generalization framework to learn high-quality feasible solutions. The proposed framework is demonstrated to tackle the two well-known dynamic NP-Hard optimization problems: multi-item capacitated lot-sizing and multi-dimensional knapsack. The results show that models trained on shorter and smaller-dimension instances can be successfully used to predict longer and larger-dimension problems with the presented item-wise expansion algorithm. The solution time can be reduced by three orders of magnitude with an average optimality gap below 0.1%. The proposed framework can be advantageous for solving dynamic mixed-integer programming problems that need to be solved instantly and repetitively. In the third study, a deep reinforcement learning-based framework is presented for solving scenario-based two-stage stochastic programming problems, which are computationally challenging to solve. A general two-stage deep reinforcement learning framework is proposed where two learning agents sequentially learn to solve each stage of a general two-stage stochastic multi-dimensional knapsack problem. The results show that solution time can be reduced significantly with a relatively small gap. Additionally, decision-making agents can be trained with a few scenarios and solve problems with a large number of scenarios. In the fourth study, a learning-based prediction-optimization framework is proposed for solving scenario-based multi-stage stochastic programs. The issue of non-anticipativity is addressed with a novel neural network architecture that is based on a neural machine translation system. Furthermore, training the models on deterministic problems is suggested instead of solving hard and time-consuming stochastic programs. In this framework, the level of variables used for the solution is iteratively reduced to eliminate infeasibility, and a heuristic based on a linear relaxation is performed to reduce the solution time. An improved item-wise expansion strategy is introduced to generalize the algorithm to tackle instances with different sizes. The results are presented in solving stochastic multi-item capacitated lot-sizing and stochastic multi-stage multi-dimensional knapsack problems. The results show that the solution time can be reduced by a factor of 599 with an optimality gap of only 0.08%. Moreover, results demonstrate that the models can be used to predict similarly structured stochastic programming problems with a varying number of periods, items, and scenarios. The frameworks presented in this dissertation can be utilized to achieve high-quality and fast solutions to repeatedly-solved problems in various industrial and business settings, such as production and inventory management, capacity planning, scheduling, airline logistics, dynamic pricing, and emergency management

Machine Learning for Multi-Layer Open and Disaggregated Optical Networks

L'abstract è presente nell'allegato / the abstract is in the attachmen

Dimensionamento e optimização da arquitetura dos nós em redes de transporte óticas

Mestrado em Engenharia Eletrónica e TelecomunicaçõesNesta dissertação é apresentada uma introdução as redes de transporte oticas multicamada. Foram caracterizados os dois elementos principais da rede: nós e ligações. Ao nível das ligações foi feita uma abordagem baseada nos seus elementos físicos principais. Ao nível dos nós foram tidos em consideração o tráfego de cliente (baixo débito) e o tráfego de linha (alto débito), bem como os componentes necessários para os transportar. A forma como o tráfego de cliente e agregado e o encaminhamento do mesmo na rede, exigem a elaboração de uma arquitetura que minimize os recursos necessários. A necessidade de otimizar este processo de dimensionamento da rede levou a construção e validação de métodos de agregação de tráfego e encaminhamento baseados em topologias lógicas da rede. Assim, proponho nesta dissertação algoritmos de agregação e encaminhamento aplicados a um software livre, previamente validados por modelos de programação linear baseados em restrições e funções objectivo adequadas a topologia pretendida. A apresentação detalhada dos resultados considerando o CAPEX, bem como a sua análise são considerados na dissertação. Por fim, são apresentadas conclusões e sugerido o trabalho científico que ainda pode ser realizado neste âmbito.In this dissertation an introduction is presented to the multilayer optical transport networks. The two main elements of the network were characterized: nodes and links. Regarding the connections it was made a shallower approach based on its key physical elements. In terms of nodes client traf- c (low bandwith) and the line tra c (high bandwith) were considered as well as the components necessary to carry them. The way the client tra c is aggregated and its forwarding in the same network requires an architecture which makes use of the minimum resources. The need of optimizing this network design process led to the construction and validation of tra c aggregation methods and routing based on logical network topologies. I therefore propose in this dissertation routing and grooming algorithms applied to a open source software, previously validated by linear programming models based on constraints and objective functions suitable to the desired topology. A detailed presentation of the results considering the CAPEX and its analysis are also taken into account. Finally, conclusions are presented and the scienti c work that can still be done in this area is suggested

Optimised Design and Analysis of All-Optical Networks

This PhD thesis presents a suite of methods for optimising design and for analysing blocking probabilities of all-optical networks. It thus contributes methodical knowledge to the field of computer assisted planning of optical networks. A two-stage greenfield optical network design optimiser is developed, based on shortest-path algorithms and a comparatively new metaheuristic called simulated allocation. It is able to handle design of all-optical mesh networks with optical cross-connects, considers duct as well as fibre and node costs, and can also design protected networks. The method is assessed through various experiments and is shown to produce good results and to be able to scale up to networks of realistic sizes. A novel method, subpath wavelength grouping, for routing connections in a multigranular all-optical network where several wavelengths can be grouped and switched at band and fibre level is presented. The method uses an unorthodox routing strategy focusing on common subpaths rather than individual connections, and strives to minimise switch port count as well as fibre usage. It is shown to produce cheaper network designs than previous methods when fibre costs are comparatively high. A new optical network concept, the synchronous optical hierarchy, is proposed, in which wavelengths are subdivided into timeslots to match the traffic granularity. Various theoretical properties of this concept are investigated and compared in simulation studies. An integer linear programming model for optical ring network design is presented. Manually designed real world ring networks are studied and it is found that the model can lead to cheaper network design. Moreover, ring and mesh network architectures are compared using real world costs, and it is found that optical cros..

Smart electric vehicle charging strategy in direct current microgrid

This thesis proposes novel electric vehicle (EV) charging strategies in DC microgrid (DCMG) for integrating network loads, EV charging/discharging and dispatchable generators (DGs) using droop control within DCMG. A novel two-stage optimization framework is deployed, which optimizes power flow in the network using droop control within DCMG and solves charging tasks with a modified Djistra algorithm. Charging tasks here are modeled as the shortest path problem considering system losses and battery degradation from the distribution system operator (DSO) and electric vehicles aggregator (EVA) respectively. Furthermore, a probabilistic distribution model is proposed to investigate the EV stochastic behaviours for a charging station including time-of-arrival (TOA), time-of-departure(TOD) and energy-to-be-charged (ETC) as well as the coupling characteristic between these parameters. Markov Chain Monte Carlo (MCMC) method is employed to establish a multi-dimension probability distribution for those load profiles and further tests show the scheme is suitable for decentralized computing of its low burn-in request, fast convergent and good parallel acceleration performance. Following this, a three-stage stochastic EV charging strategy is designed to plug the probabilistic distribution model into the optimization framework, which becomes the first stage of the framework. Subsequently, an optimal power flow (OPF) model in the DCMG is deployed where the previous deterministic model is deployed in the second stage which stage one and stage two are combined as a chance-constrained problem in stage three and solved as a random walk problem. Finally, this thesis investigates the value of EV integration in the DCMG. The results obtained show that with smart control of EV charging/discharging, not only EV charging requests can be satisfied, but also network performance like peak valley difference can be improved by ancillary services. Meanwhile, both system loss and battery degradation from DSO and EVA can be minimized.Open Acces

Spectrum Allocation Algorithms for Cognitive Radio Mesh Networks

Empowered by the cognitive radio technology, and motivated by the sporadic channel utilization, both spatially and temporally, dynamic spectrum access networks (also referred to as cognitive radio networks and next generation wireless networks) have emerged as a solution to improve spectrum utilization and provide more flexibility to wireless communication. A cognitive radio network is composed of wireless users, referred to as secondary users, which are allowed to use licensed spectrum bands as long as their are no primary, licensed, users occupying the channel in their vicinity. This restricted spectrum access strategy leads to heterogeneity in channel availability among secondary users. This heterogeneity forms a significant source of performance degradation for cognitive radio networks, and poses a great challenge on protocol design. In this dissertation, we propose spectrum allocation algorithms that take into consideration the heterogeneity property and its effect on the network performance. The spectrum allocation solutions proposed in this dissertation address three major objectives in cognitive radio mesh networks. The first objective is maximizing the network coverage, in terms of the total number of served clients, and at the same time simplifying the communication coordination function. To address this objective, we proposed a received based channel allocation strategy that alleviates the need for a common control channel, thus simplifying the coordination function, and at the same time maximizes the number of clients served with link reliability guarantees. We show the superiority of the proposed allocation strategy over other existing strategies. The second objective is improving the multicast throughput to compensate for the performance degradation caused by channel heterogeneity. We proposed a scheduling algorithm that schedules multicast transmissions over both time and frequency and integrates that with the use of network coding. This algorithm achieves a significant gain, measured as the reduction in the total multicast time, as the simulation results prove. We also proposed a failure recovery algorithm that can adaptively adjust the schedule in response to temporary changes in channel availability. The last objective is minimizing the effect of channel switching on the end-to-end delay and network throughput. Channel switching can be a significant source of delay and bandwidth wastage, especially if the secondary users are utilizing a wide spectrum band. To address this issue, we proposed an on-demand multicast routing algorithm for cognitive radio mesh networks based on dynamic programming. The algorithm finds the best available route in terms of end-to-end delay, taking into consideration the switching latency at individual nodes and the transmission time on different channels. We also presented the extensibility of the proposed algorithm to different routing metric. Furthermore, a route recovery algorithm that takes into consideration the overhead of rerouting and the route cost was also proposed. The gain of these algorithms was proved by simulation