Multipurpose Programmable Integrated Photonics: Principles and Applications

[ES] En los últimos años, la fotónica integrada programable ha evolucionado desde considerarse un paradigma nuevo y prometedor para implementar la fotónica a una escala más amplia hacia convertirse una realidad sólida y revolucionaria, capturando la atención de numerosos grupos de investigación e industrias. Basada en el mismo fundamento teórico que las matrices de puertas lógicas programables en campo (o FPGAs, en inglés), esta tecnología se sustenta en la disposición bidimensional de bloques unitarios de lógica programable (en inglés: PUCs) que -mediante una programación adecuada de sus actuadores de fase- pueden implementar una gran variedad de funcionalidades que pueden ser elaboradas para operaciones básicas o más complejas en muchos campos de aplicación como la inteligencia artificial, el aprendizaje profundo, los sistemas de información cuántica, las telecomunicaciones 5/6-G, en redes de conmutación, formando interconexiones en centros de datos, en la aceleración de hardware o en sistemas de detección, entre otros. En este trabajo, nos dedicaremos a explorar varias aplicaciones software de estos procesadores en diferentes diseños de chips. Exploraremos diferentes enfoques de vanguardia basados en la optimización computacional y la teoría de grafos para controlar y configurar con precisión estos dispositivos. Uno de estos enfoques, la autoconfiguración, consiste en la síntesis automática de circuitos ópticos -incluso en presencia de efectos parasitarios como distribuciones de pérdidas no uniformes a lo largo del diseño hardware, o bajo interferencias ópticas y eléctricas- sin conocimiento previo sobre el estado del dispositivo. Hay ocasiones, sin embargo, en las que el acceso a esta información puede ser útil. Las herramientas de autocalibración y autocaracterización nos permiten realizar una comprobación rápida del estado de nuestro procesador fotónico, lo que nos permite extraer información útil como la corriente eléctrica que suministrar a cada actuador de fase para cambiar el estado de su PUC correspondiente, o las pérdidas de inserción de cada unidad programable y de las interconexiones ópticas que rodean a la estructura. Estos mecanismos no solo nos permiten identificar rápidamente cualquier PUC o región del chip defectuosa en nuestro diseño, sino que también revelan otra alternativa para programar circuitos fotónicos en nuestro diseño a partir de valores de corriente predefinidos. Estas estrategias constituyen un paso significativo para aprovechar todo el potencial de estos dispositivos. Proporcionan soluciones para manejar cientos de variables y gestionar simultáneamente múltiples acciones de configuración, una de las principales limitaciones que impiden que esta tecnología se extienda y se convierta en disruptiva en los próximos años.[CA] En els darrers anys, la fotònica integrada programable ha evolucionat des de considerarse un paradigma nou i prometedor per implementar la fotònica a una escala més ampla cap a convertir-se en una realitat sòlida i revolucionària, capturant l'atenció de nombrosos grups d'investigaciò i indústries. Basada en el mateix fonament teòric que les matrius de portes lògiques programable en camp (o FPGAs, en anglès), aquesta tecnología es sustenta en la disposición bidimensional de blocs units lògics programables (en anglès: PUCs) que -mitjançant una programación adequada dels seus actuadors de fase- poden implementar una gran varietat de funcionalitats que poden ser elaborades per a operacions bàsiques o més complexes en molts camps d'aplicació com la intel·ligència artificial, l'aprenentatge profund, els sistemes d'informació quàntica, les telecomunicacions 5/6-G, en xarxes de comutació, formant interconnexions en centres de dades, en l'acceleració de hardware o en sistemes de detecció, entre d'altres. En aquest treball, ens dedicarem a explorar diverses capatitats de programari d'aquests processadors en diferents dissenys de xips. Explorem diferents enfocaments de vanguardia basats en l'optimització computacional i la teoría de grafs per controlar i configurar amb precisió aquests dispositius. Un d'aquests enfocaments, l'autoconfiguració, tracta de la síntesi automática de circuits òptics -fins i tot en presencia d'efectes parasitaris com ara pèrdues no uniformes o crosstalk òptic i elèctric- sense cap coneixement previ sobre l'estat del dispositiu. Tanmateix, hi ha ocasions en les quals l'accés a aquesta información pot ser útil. Les eines d'autocalibració i autocaracterització ens permeten realizar una comprovació ràpida de l'estat del nostre procesador fotònic, el que ens permet obtener informació útil com la corrent eléctrica necessària per alimentar cada actuador de fase per canviar l'estat del seu PUC corresponent o la pèrdua d'inserció de cada unitat programable i de les interconnexions òptiques que envolten l'estructura. Aquests mecanisms no només ens permeten identificar ràpidament qualsevol PUC o área del xip defectuosa en el nostre disseny , sinó que també ens mostren una altra alternativa per programar circuits fotònics en el nostre disseny a partir de valors de corrent predefinits. Aquestes estratègies constitueixen un pas gegant per a aprofitar tot el potencial d'aquests dispositius. Proporcionen solucions per a gestionar centenars de variables i alhora administrar múltiples accions de configuració, una de les principals limitacions que impideixen que aquesta tecnología esdevingui disruptiva en els pròxims anys.[EN] In recent years, programmable integrated photonics (PIP) has evolved from a promising, new paradigm to deploy photonics to a larger scale to a solid, revolutionary reality, bringing up the attention of numerous research and industry players. Based on the same theoretical foundations than field-programmable gate arrays (FPGAs), this technology relies on common, two-dimensional integrated optical hardware configurations based on the interconnection of programmable unit cells (PUCs), which -by suitable programming of their phase actuators- can implement a variety of functionalities that can be elaborated for basic or more complex operation in many application fields, such as artificial intelligence, deep learning, quantum information systems, 5/6-G telecommunications, switching, data center interconnections, hardware acceleration and sensing, amongst others. In this work, we will dedicate ourselves to explore several software capabilities of these processors under different chip designs. We explore different cutting-edge approaches based on computational optimization and graph theory to precisely control and configure these devices. One of these, self-configuration, deals with the automated synthesis of optical circuit configurations -even in presence of parasitic effects such as nonuniform losses, optical and electrical crosstalk- without any need for prior knowledge about hardware state. There are occasions, though, in which accessing to this information may be of use. Self-calibration and self-characterization tools allow us to perform a quick check to our photonic processor's status, allowing us to retrieve useful pieces of information such as the electrical current needed to supply to each phase actuator to change its corresponding PUC state arbitrarily or the insertion loss of every unit cell and optical interconnection surrounding the structure. These mechanisms not only allow us to quickly identify any malfunctioning PUCs or chip areas in our design, but also reveal another alternative to program photonic circuits in our design from current pre-sets. These strategies constitute a gigantic step to unleash all the potential of these devices. They provide solutions to handle with hundreds of variables and simultaneously manage multiple configuration actions, one of the main limitations that prevent this technology to scale up and become disruptive in the years to come.López Hernández, A. (2023). Multipurpose Programmable Integrated Photonics: Principles and Applications [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/19686

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

Clock Synchronisation Assisted Clock and Data Recovery for Sub-Nanosecond Data Centre Optical Switching

In current `Cloud' data centres, switching of data between servers is performed using deep hierarchies of interconnected electronic packet switches. Demand for network bandwidth from emerging data centre workloads, combined with the slowing of silicon transistor scaling, is leading to a widening gap between data centre traffic demand and electronically-switched data centre network capacity. All-optical switches could offer a future-proof alternative, with potentially under a third of the power consumption and cost of electronically-switched networks. However, the effective bandwidth of optical switches depends on their overall switching time. This is dominated by the clock and data recovery (CDR) locking time, which takes hundreds of nanoseconds in commercial receivers. Current data centre traffic is dominated by small packets that transmit in tens of nanoseconds, leading to low effective bandwidth, as a high proportion of receiver time is spent performing CDR locking instead of receiving data, removing the benefits of optical switching. High-performance optical switching requires sub-nanosecond CDR locking time to overcome this limitation. This thesis proposes, models, and demonstrates clock synchronisation assisted CDR, which can achieve this. This approach uses clock synchronisation to simplify the complexity of CDR versus previous asynchronous approaches. An analytical model of the technique is first derived that establishes its potential viability. Following this, two approaches to clock synchronisation assisted CDR are investigated: 1. Clock phase caching, which uses clock phase storage and regular updates in a 2km intra-building scale data centre network interconnected by single-mode optical fibre. 2. Single calibration clock synchronisation assisted CDR}, which leverages the 20 times lower thermal sensitivity of hollow core optical fibre versus single-mode fibre to synchronise a 100m cluster scale data centre network, with a single initial phase calibration step. Using a real-time FPGA-based optical switch testbed, sub-nanosecond CDR locking time was demonstrated for both approaches

When Stuck, Flip a Coin:New Algorithms for Large-Scale Tasks

Many modern services need to routinely perform tasks on a large scale. This prompts us to consider the following question: How can we design efficient algorithms for large-scale computation? In this thesis, we focus on devising a general strategy to address the above question. Our approaches use tools from graph theory and convex optimization, and prove to be very effective on a number of problems that exhibit locality. A recurring theme in our work is to use randomization to obtain simple and practical algorithms. The techniques we developed enabled us to make progress on the following questions: - Parallel Computation of Approximately Maximum Matchings. We put forth a new approach to computing $O(1)$-approximate maximum matchings in the Massively Parallel Computation (MPC) model. In the regime in which the memory per machine is $\Theta(n)$, i.e., linear in the size of the vertex-set, our algorithm requires only $O((\log \log{n})^2)$ rounds of computations. This is an almost exponential improvement over the barrier of $\Omega(\log {n})$ rounds that all the previous results required in this regime. - Parallel Computation of Maximal Independent Sets. We propose a simple randomized algorithm that constructs maximal independent sets in the MPC model. If the memory per machine is $\Theta(n)$ our algorithm runs in $O(\log \log{n})$ MPC-rounds. In the same regime, all the previously known algorithms required $O(\log{n})$ rounds of computation. - Network Routing under Link Failures. We design a new protocol for stateless message-routing in $k$-connected graphs. Our routing scheme has two important features: (1) each router performs the routing decisions based only on the local information available to it; and, (2) a message is delivered successfully even if arbitrary $k-1$ links have failed. This significantly improves upon the previous work of which the routing schemes tolerate only up to $k/2 - 1$ failed links in $k$-connected graphs. - Streaming Submodular Maximization under Element Removals. We study the problem of maximizing submodular functions subject to cardinality constraint $k$, in the context of streaming algorithms. In a regime in which up to $m$ elements can be removed from the stream, we design an algorithm that provides a constant-factor approximation for this problem. At the same time, the algorithm stores only $O(k \log^2{k} + m \log^3{k})$ elements. Our algorithm improves quadratically upon the prior work, that requires storing $O(k \cdot m)$ many elements to solve the same problem. - Fast Recovery for the Separated Sparsity Model. In the context of compressed sensing, we put forth two recovery algorithms of nearly-linear time for the separated sparsity signals (that naturally model neural spikes). This improves upon the previous algorithm that had a quadratic running time. We also derive a refined version of the natural dynamic programming (DP) approach to the recovery of the separated sparsity signals. This DP approach leads to a recovery algorithm that runs in linear time for an important class of separated sparsity signals. Finally, we consider a generalization of these signals into two dimensions, and we show that computing an exact projection for the two-dimensional model is NP-hard