Efficient schemes to size transistors for optimal delay by solving fanout branches with balancing algorithm

High performance digital system requires minimal logic and properly sized transistor to operate in all PVT corners. Specifically, high-speed data-path design is mostly about optimizing the system for better timing. In this work, the author proposed a better timing model to analyze parallel data-paths better for performance comparison. Moreover, a novel transistor sizing technique is also proposed as part of the work to minimize delay in parallel data-path circuits in the presence of practical wire capacitance. With this technique it is easier to calculate the optimal capacitance distribution in a fanout branch path that equalizes the delays in all branches as well as minimizes the overall delay starting from the primary inputs to the primary outputs of a circuit. The problem is widely termed as the "Load distribution problem at branch". A collection of fast algorithms were designed to accurately solve the load distribution problem for branch in digital circuits for optimal delay. The author used prior work on Unified Logical Effort[1] as a tool for delay estimation and transistor sizing. This research work also shows the impact of branching on critical path. Experiments are run on industry standard circuits using different types of tools developed to model the circuit. The new developed theories are tested on the circuit models , that are also included in this work

An Approach for Supporting Ad-hoc Modifications in Distributed Workflow Management Systems

Supporting enterprise-wide or even cross-organizational business processes is a characteristic challenge for any workflow management system (WfMS). Scalability at the presence of high loads as well as the capability to dynamically modify running workflow (WF) instances (e.g., to cope with exceptional situations) are essential requirements in this context. Should the latter one, in particular, not be met, the WfMS will not have the necessary flexibility to cover the wide range of process-oriented applications deployed in many organizations. Scalability and flexibility have, for the most part, been treated separately in the relevant literature thus far. Even though they are basic needs for a WfMS, the requirements related with them are totally different. To achieve satisfactory scalability, on the one hand, the system needs to be designed such that a workflow instance can be controlled by several WF servers that are as independent from each other as possible. Yet dynamic WF modifications, on the other hand, necessitate a (logical) central control instance which knows the current and global state of a WF instance. For the first time, this paper presents methods which allow ad-hoc modifications (e.g., to insert, delete, or shift steps) to be performed in a distributed WfMS; i.e., in a WfMS with partitioned WF execution graphs and distributed WF control. It is especially noteworthy that the system succeeds in realizing the full functionality as given in the central case while, at the same time, achieving extremely favorable behavior with respect to communication costs

Machine-learning-aided warm-start of constraint generation methods for online mixed-integer optimization

Mixed Integer Linear Programs (MILP) are well known to be NP-hard problems in general. Even though pure optimization-based methods, such as constraint generation, are guaranteed to provide an optimal solution if enough time is given, their use in online applications is still a great challenge due to their usual excessive time requirements. To alleviate their computational burden, some machine learning techniques have been proposed in the literature, using the information provided by previously solved MILP instances. Unfortunately, these techniques report a non-negligible percentage of infeasible or suboptimal instances. By linking mathematical optimization and machine learning, this paper proposes a novel approach that speeds up the traditional constraint generation method, preserving feasibility and optimality guarantees. In particular, we first identify offline the so-called invariant constraint set of past MILP instances. We then train (also offline) a machine learning method to learn an invariant constraint set as a function of the problem parameters of each instance. Next, we predict online an invariant constraint set of the new unseen MILP application and use it to initialize the constraint generation method. This warm-started strategy significantly reduces the number of iterations to reach optimality, and therefore, the computational burden to solve online each MILP problem is significantly reduced. Very importantly, the proposed methodology inherits the feasibility and optimality guarantees of the traditional constraint generation method. The computational performance of the proposed approach is quantified through synthetic and real-life MILP applications

Unified field multiplier for GF(p) and GF(2 n) with novel digit encoding

In recent years, there has been an increase in demand for unified field multipliers for Elliptic Curve Cryptography in the electronics industry because they provide flexibility for customers to choose between Prime (GF(p)) and Binary (GF(2")) Galois Fields. Also, having the ability to carry out arithmetic over both GF(p) and GF(2") in the same hardware provides the possibility of performing any cryptographic operation that requires the use of both fields. The unified field multiplier is relatively future proof compared with multipliers that only perform arithmetic over a single chosen field. The security provided by the architecture is also very important. It is known that the longer the key length, the more susceptible the system is to differential power attacks due to the increased amount of data leakage. Therefore, it is beneficial to design hardware that is scalable, so that more data can be processed per cycle. Another advantage of designing a multiplier that is capable of dealing with long word length is improvement in performance in terms of delay, because less cycles are needed. This is very important because typical elliptic curve cryptography involves key size of 160 bits. A novel unified field radix-4 multiplier using Montgomery Multiplication for the use of G(p) and GF(2") has been proposed. This design makes use of the unexploited state in number representation for operation in GF(2") where all carries are suppressed. The addition is carried out using a modified (4:2) redundant adder to accommodate the extra 1 * state. The proposed adder and the partial product generator design are capable of radix-4 operation, which reduces the number of computation cycles required. Also, the proposed adder is more scalable than existing designs.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Inferring Geodesic Cerebrovascular Graphs: Image Processing, Topological Alignment and Biomarkers Extraction

A vectorial representation of the vascular network that embodies quantitative features - location, direction, scale, and bifurcations - has many potential neuro-vascular applications. Patient-specific models support computer-assisted surgical procedures in neurovascular interventions, while analyses on multiple subjects are essential for group-level studies on which clinical prediction and therapeutic inference ultimately depend. This first motivated the development of a variety of methods to segment the cerebrovascular system. Nonetheless, a number of limitations, ranging from data-driven inhomogeneities, the anatomical intra- and inter-subject variability, the lack of exhaustive ground-truth, the need for operator-dependent processing pipelines, and the highly non-linear vascular domain, still make the automatic inference of the cerebrovascular topology an open problem. In this thesis, brain vessels’ topology is inferred by focusing on their connectedness. With a novel framework, the brain vasculature is recovered from 3D angiographies by solving a connectivity-optimised anisotropic level-set over a voxel-wise tensor field representing the orientation of the underlying vasculature. Assuming vessels joining by minimal paths, a connectivity paradigm is formulated to automatically determine the vascular topology as an over-connected geodesic graph. Ultimately, deep-brain vascular structures are extracted with geodesic minimum spanning trees. The inferred topologies are then aligned with similar ones for labelling and propagating information over a non-linear vectorial domain, where the branching pattern of a set of vessels transcends a subject-specific quantized grid. Using a multi-source embedding of a vascular graph, the pairwise registration of topologies is performed with the state-of-the-art graph matching techniques employed in computer vision. Functional biomarkers are determined over the neurovascular graphs with two complementary approaches. Efficient approximations of blood flow and pressure drop account for autoregulation and compensation mechanisms in the whole network in presence of perturbations, using lumped-parameters analog-equivalents from clinical angiographies. Also, a localised NURBS-based parametrisation of bifurcations is introduced to model fluid-solid interactions by means of hemodynamic simulations using an isogeometric analysis framework, where both geometry and solution profile at the interface share the same homogeneous domain. Experimental results on synthetic and clinical angiographies validated the proposed formulations. Perspectives and future works are discussed for the group-wise alignment of cerebrovascular topologies over a population, towards defining cerebrovascular atlases, and for further topological optimisation strategies and risk prediction models for therapeutic inference. Most of the algorithms presented in this work are available as part of the open-source package VTrails

A novel multi-level and community-based agent ecosystem to support customers dynamic decision-making in smart grids

Electrical systems have evolved at a fast pace over the past years, particularly in response to the current environmental and climate challenges. Consequently, the European Union and the United Nations have encouraged the development of a more sustainable energy strategy. This strategy triggered a paradigm shift in energy consumption and production, which becoming increasingly distributed, resulted in the development and emergence of smart energy grids. Multi-agent systems are one of the most widely used artificial intelligence concepts in smart grids. Both multi-agent systems and smart grids are distributed, so there is correspondence between the used technology and the network's complex reality. Due to the wide variety of multi-agent systems applied to smart grids, which typically have very specific goals, the ability to model the network as a whole may be compromised, as communication between systems is typically non-existent. This dissertation, therefore, proposes an agent-based ecosystem to model smart grids in which different agent-based systems can coexist. This dissertation aims to conceive, implement, test, and validate a new agent-based ecosystem, entitled A4SG (agent-based ecosystem for smart grids modelling), which combines the concepts of multi-agent systems and agent communities to enable the modelling and representation of smart grids and the entities that compose them. The proposed ecosystem employs an innovative methodology for managing static or dynamic interactions present in smart grids. The creation of a solution that allows the integration of existing systems into an ecosystem, enables the representation of smart grids in a realistic and comprehensive manner. A4SG integrates several functionalities that support the ecosystem's management, also conceived, implemented, tested, and validated in this dissertation. Two mobility functionalities are proposed: one that allows agents to move between physical machines and another that allows "virtual" mobility, where agents move between agent communities to improve the context for the achievement of their objectives. In order to prevent an agent from becoming overloaded, a novel functionality is proposed to enable the creation of agents that function as extensions of the main agent (i.e., branch agents), allowing the distribution of objectives among the various extensions of the main agent. Several case studies, which test the proposed services and functionalities individually and the ecosystem as a whole, were used to test and validate the proposed solution. These case studies were conducted in realistic contexts using data from multiple sources, including energy communities. The results indicate that the used methodologies can increase participation in demand response events, increasing the fitting between consumers and aggregators from 12 % to 69 %, and improve the strategies used in energy transaction markets, allowing an energy community of 50 customers to save 77.0 EUR per week.Os últimos anos têm sido de mudança nos sistemas elétricos, especialmente devido aos atuais desafios ambientais e climáticos. A procura por uma estratégia mais sustentável para o domínio da energia tem sido promovida pela União Europeia e pela Organização das Nações Unidas. A mudança de paradigma no que toca ao consumo e produção de energia, que acontece, cada vez mais, de forma distribuída, tem levado à emergência das redes elétricas inteligentes. Os sistemas multi-agente são um dos conceitos, no domínio da inteligência artificial, mais aplicados em redes inteligentes. Tanto os sistemas multi-agente como as redes inteligentes têm uma natureza distribuída, existindo por isso um alinhamento entre a tecnologia usada e a realidade complexa da rede. Devido a existir uma vasta oferta de sistemas multi-agente aplicados a redes inteligentes, normalmente com objetivos bastante específicos, a capacidade de modelar a rede como um todo pode ficar comprometida, porque a comunicação entre sistemas é, geralmente, inexistente. Por isso, esta dissertação propõe um ecossistema baseado em agentes para modelar as redes inteligentes, onde vários sistemas de agentes coexistem. Esta dissertação pretende conceber, implementar, testar, e validar um novo ecossistema multiagente, intitulado A4SG (agent-based ecosystem for smart grids modelling), que combina os conceitos de sistemas multi-agente e comunidades de agentes, permitindo a modelação e representação de redes inteligentes e das suas entidades. O ecossistema proposto utiliza uma metodologia inovadora para gerir as interações presentes nas redes inteligentes, sejam elas estáticas ou dinâmicas. A criação de um ecossistema que permite a integração de sistemas já existentes, cria a possibilidade de uma representação realista e detalhada das redes de energia. O A4SG integra diversas funcionalidades, também estas concebidas, implementadas, testadas, e validadas nesta dissertação, que suportam a gestão do próprio ecossistema. São propostas duas funcionalidades de mobilidade, uma que permite aos agentes mover-se entre máquinas físicas, e uma que permite uma mobilidade “virtual”, onde os agentes se movem entre comunidades de agentes, de forma a melhorar o contexto para a execução dos seus objetivos. É também proposta uma nova funcionalidade que permite a criação de agentes que funcionam como uma extensão de um agente principal, com o objetivo de evitar a sobrecarga de um agente, permitindo a distribuição de objetivos entre as várias extensões do agente principal. A solução proposta foi testada e validada por vários casos de estudo, que testam os serviços e funcionalidades propostas individualmente, e o ecossistema como um todo. Estes casos de estudo foram executados em contextos realistas, usando dados provenientes de diversas fontes, tais como comunidades de energia. Os resultados demonstram que as metodologias utilizadas podem melhorar a participação em eventos de demand response, subindo a adequação entre consumidores e agregadores de 12 % para 69 %, e melhorar as estratégias utilizadas em mercados de transações de energia, permitindo a uma comunidade de energia com 50 consumidores poupar 77,0 EUR por semana

Unified field multiplier for GF(p) and GF(2 n) with novel digit encoding

In recent years, there has been an increase in demand for unified field multipliers for Elliptic Curve Cryptography in the electronics industry because they provide flexibility for customers to choose between Prime (GF(p)) and Binary (GF(2')) Galois Fields. Also, having the ability to carry out arithmetic over both GF(p) and GF(2') in the same hardware provides the possibility of performing any cryptographic operation that requires the use of both fields. The unified field multiplier is relatively future proof compared with multipliers that only perform arithmetic over a single chosen field. The security provided by the architecture is also very important. It is known that the longer the key length, the more susceptible the system is to differential power attacks due to the increased amount of data leakage. Therefore, it is beneficial to design hardware that is scalable, so that more data can be processed per cycle. Another advantage of designing a multiplier that is capable of dealing with long word length is improvement in performance in terms of delay, because less cycles are needed. This is very important because typical elliptic curve cryptography involves key size of 160 bits. A novel unified field radix-4 multiplier using Montgomery Multiplication for the use of G(p) and GF(2') has been proposed. This design makes use of the unexploited state in number representation for operation in GF(2') where all carries are suppressed. The addition is carried out using a modified (4:2) redundant adder to accommodate the extra 1 * state. The proposed adder and the partial product generator design are capable of radix-4 operation, which reduces the number of computation cycles required. Also, the proposed adder is more scalable than existing designs

POOR MAN’S TRACE CACHE: A VARIABLE DELAY SLOT ARCHITECTURE

We introduce a novel fetch architecture called Poor Man’s Trace Cache (PMTC). PMTC constructs taken-path instruction traces via instruction replication in static code and inserts them after unconditional direct and select conditional direct control transfer instructions. These traces extend to the end of the cache line. Since available space for trace insertion may vary by the position of the control transfer instruction within the line, we refer to these fetch slots as variable delay slots. This approach ensures traces are fetched along with the control transfer instruction that initiated the trace. Branch, jump and return instruction semantics as well as the fetch unit are modified to utilize traces in delay slots. PMTC yields the following benefits: 1. Average fetch bandwidth increases as the front end can fetch across taken control transfer instructions in a single cycle. 2. The dynamic number of instruction cache lines fetched by the processor is reduced as multiple non contiguous basic blocks along a given path are encountered in one fetch cycle. 3. Replication of a branch instruction along multiple paths provides path separability for branches, which positively impacts branch prediction accuracy. PMTC mechanism requires minimal modifications to the processor’s fetch unit and the trace insertion algorithm can easily be implemented within the assembler without compiler support