Structure discovery techniques for circuit design and process model visualization

Graphs are one of the most used abstractions in many knowledge fields because of the easy and flexibility by which graphs can represent relationships between objects. The pervasiveness of graphs in many disciplines means that huge amounts of data are available in graph form, allowing many opportunities for the extraction of useful structure from these graphs in order to produce insight into the data. In this thesis we introduce a series of techniques to resolve well-known challenges in the areas of digital circuit design and process mining. The underlying idea that ties all the approaches together is discovering structures in graphs. We show how many problems of practical importance in these areas can be solved utilizing both common and novel structure mining approaches. In the area of digital circuit design, this thesis proposes automatically discovering frequent, repetitive structures in a circuit netlist in order to improve the quality of physical planning. These structures can be used during floorplanning to produce regular designs, which are known to be highly efficient and economical. At the same time, detecting these repeating structures can exponentially reduce the total design time. The second focus of this thesis is in the area of the visualization of process models. Process mining is a recent area of research which centers on studying the behavior of real-life systems and their interactions with the environment. Complicated process models, however, hamper this goal. By discovering the important structures in these models, we propose a series of methods that can derive visualization-friendly process models with minimal loss in accuracy. In addition, and combining the areas of circuit design and process mining, this thesis opens the area of specification mining in asynchronous circuits. Instead of the usual design flow, which involves synthesizing circuits from specifications, our proposal discovers specifications from implemented circuits. This area allows for many opportunities for verification and re-synthesis of asynchronous circuits. The proposed methods have been tested using real-life benchmarks, and the quality of the results compared to the state-of-the-art.Els grafs són una de les representacions abstractes més comuns en molts camps de recerca, gràcies a la facilitat i flexibilitat amb la que poden representar relacions entre objectes. Aquesta popularitat fa que una gran quantitat de dades es puguin trobar en forma de graf, i obre moltes oportunitats per a extreure estructures d'aquest grafs, útils per tal de donar una intuïció millor de les dades subjacents. En aquesta tesi introduïm una sèrie de tècniques per resoldre reptes habitualment trobats en les àrees de disseny de circuits digitals i mineria de processos industrials. La idea comú sota tots els mètodes proposats es descobrir automàticament estructures en grafs. En la tesi es mostra que molts problemes trobats a la pràctica en aquestes àrees poden ser resolts utilitzant nous mètodes de descobriment d'estructures. En l'àrea de disseny de circuits, proposem descobrir, automàticament, estructures freqüents i repetitives en les definicions del circuit per tal de millorar la qualitat de les etapes posteriors de planificació física. Les estructures descobertes poden fer-se servir durant la planificació per produir dissenys regulars, que son molt més econòmics d'implementar. Al mateix temps, la descoberta i ús d'aquestes estructures pot reduir exponencialment el temps total de disseny. El segon punt focal d'aquesta tesi és en l'àrea de la visualització de models de processos industrials. La mineria de processos industrials es un tema jove de recerca que es centra en estudiar el comportament de sistemes reals i les interaccions d'aquests sistemes amb l'entorn. No obstant, quan d'aquest anàlisi s'obtenen models massa complexos visualment, l'estudi n'és problemàtic. Proposem una sèrie de mètodes que, gràcies al descobriment automàtic de les estructures més importants, poden generar models molt més fàcils de visualitzar que encara descriuen el comportament del sistema amb gran precisió. Combinant les àrees de disseny de circuits i mineria de processos, aquesta tesi també obre un nou tema de recerca: la mineria d'especificacions per circuits asíncrons. En l'estil de disseny asíncron habitual, sintetitzadors automàtics generen circuits a partir de les especificacions. En aquesta tesi proposem el pas invers: descobrir automàticament les especificacions de circuits ja implementats. Així, creem noves oportunitats per a la verificació i la re-síntesi de circuits asíncrons. Els mètodes proposats en aquesta tesi s'han validat fent servir dades obtingudes d'aplicacions pràctiques, i en comparem els resultats amb els mètodes existents

Online learning on the programmable dataplane

This thesis makes the case for managing computer networks with datadriven methods automated statistical inference and control based on measurement data and runtime observations—and argues for their tight integration with programmable dataplane hardware to make management decisions faster and from more precise data. Optimisation, defence, and measurement of networked infrastructure are each challenging tasks in their own right, which are currently dominated by the use of hand-crafted heuristic methods. These become harder to reason about and deploy as networks scale in rates and number of forwarding elements, but their design requires expert knowledge and care around unexpected protocol interactions. This makes tailored, per-deployment or -workload solutions infeasible to develop. Recent advances in machine learning offer capable function approximation and closed-loop control which suit many of these tasks. New, programmable dataplane hardware enables more agility in the network— runtime reprogrammability, precise traffic measurement, and low latency on-path processing. The synthesis of these two developments allows complex decisions to be made on previously unusable state, and made quicker by offloading inference to the network. To justify this argument, I advance the state of the art in data-driven defence of networks, novel dataplane-friendly online reinforcement learning algorithms, and in-network data reduction to allow classification of switchscale data. Each requires co-design aware of the network, and of the failure modes of systems and carried traffic. To make online learning possible in the dataplane, I use fixed-point arithmetic and modify classical (non-neural) approaches to take advantage of the SmartNIC compute model and make use of rich device local state. I show that data-driven solutions still require great care to correctly design, but with the right domain expertise they can improve on pathological cases in DDoS defence, such as protecting legitimate UDP traffic. In-network aggregation to histograms is shown to enable accurate classification from fine temporal effects, and allows hosts to scale such classification to far larger flow counts and traffic volume. Moving reinforcement learning to the dataplane is shown to offer substantial benefits to stateaction latency and online learning throughput versus host machines; allowing policies to react faster to fine-grained network events. The dataplane environment is key in making reactive online learning feasible—to port further algorithms and learnt functions, I collate and analyse the strengths of current and future hardware designs, as well as individual algorithms

Proceedings of the 2018 Canadian Society for Mechanical Engineering (CSME) International Congress

Published proceedings of the 2018 Canadian Society for Mechanical Engineering (CSME) International Congress, hosted by York University, 27-30 May 2018