Doctor of Philosophy

dissertationRecent breakthroughs in silicon photonics technology are enabling the integration of optical devices into silicon-based semiconductor processes. Photonics technology enables high-speed, high-bandwidth, and high-fidelity communications on the chip-scale-an important development in an increasingly communications-oriented semiconductor world. Significant developments in silicon photonic manufacturing and integration are also enabling investigations into applications beyond that of traditional telecom: sensing, filtering, signal processing, quantum technology-and even optical computing. In effect, we are now seeing a convergence of communications and computation, where the traditional roles of optics and microelectronics are becoming blurred. As the applications for opto-electronic integrated circuits (OEICs) are developed, and manufacturing capabilities expand, design support is necessary to fully exploit the potential of this optics technology. Such design support for moving beyond custom-design to automated synthesis and optimization is not well developed. Scalability requires abstractions, which in turn enables and requires the use of optimization algorithms and design methodology flows. Design automation represents an opportunity to take OEIC design to a larger scale, facilitating design-space exploration, and laying the foundation for current and future optical applications-thus fully realizing the potential of this technology. This dissertation proposes design automation for integrated optic system design. Using a buildingblock model for optical devices, we provide an EDA-inspired design flow and methodologies for optical design automation. Underlying these flows and methodologies are new supporting techniques in behavioral and physical synthesis, as well as device-resynthesis techniques for thermal-aware system integration. We also provide modeling for optical devices and determine optimization and constraint parameters that guide the automation techniques. Our techniques and methodologies are then applied to the design and optimization of optical circuits and devices. Experimental results are analyzed to evaluate their efficacy. We conclude with discussions on the contributions and limitations of the approaches in the context of optical design automation, and describe the tremendous opportunities for future research in design automation for integrated optics

Support-reducing decomposition for FPGA mapping

Decomposition is a technology-independent process, in which a large complex function is broken into smaller, less complex functions. The costs of two-level or factored-form representations (cubes and literals) are used in most decomposition methods, as they have a high correlation with the area of cell-based designs. However, this correlation is weaker for field-programmable gate arrays (FPGAs) based on look-up tables. Furthermore, local optimizations have limited power due to the structural bias of the circuit descriptions. This paper tries to reduce the structural biasing by remapping the LUT network and decomposing the derived functions using the support as cost function. The proposed method improves the FPGA mapping results of a commercial tool for the 20 largest MCNC benchmarks, with gains of 28% in delay plus 18% in area when targeting delay, and a reduction of 28% in area plus 14% in delay with area as cost function. Results with 23% less area and 6% less delay are obtained after physical synthesis (post place-and-route). Moreover, 12 of the best known results for delay (and 3 for area) of the EPFL benchmarks are improved.Peer ReviewedPostprint (author's final draft

Transparent In-Circuit Assertions for FPGAs

Commonly used in software design, assertions are statements placed into a design to ensure that its behaviour matches that expected by a designer. Although assertions apply equally to hardware design, they are typically supported only for logic simulation, and discarded prior to physical implementation. We propose a new HDL-agnostic language for describing latency-insensitive assertions and novel methods to add such assertions transparently to an already placed-and-routed circuit without affecting the existing design. We also describe how this language and associated methods can be used to implement semi-transparent exception handling. The key to our work is that by treating hardware assertions and exceptions as being oblivious or less sensitive to latency, assertion logic need only use spare FPGA resources. We use network-flow techniques to route necessary signals to assertions via spare flip-flops, eliminating any performance degradation, even on large designs (92% of slices in one test). Experimental evaluation shows zero impact on critical-path delay, even on large benchmarks operating above 200MHz, at the cost of a small power penalty

Hybrid Open-set Segmentation with Synthetic Negative Data

Open-set segmentation is often conceived by complementing closed-set classification with anomaly detection. Existing dense anomaly detectors operate either through generative modelling of regular training data or by discriminating with respect to negative training data. These two approaches optimize different objectives and therefore exhibit different failure modes. Consequently, we propose the first dense hybrid anomaly score that fuses generative and discriminative cues. The proposed score can be efficiently implemented by upgrading any semantic segmentation model with translation-equivariant estimates of data likelihood and dataset posterior. Our design is a remarkably good fit for efficient inference on large images due to negligible computational overhead over the closed-set baseline. The resulting dense hybrid open-set models require negative training images that can be sampled either from an auxiliary negative dataset or from a jointly trained generative model. We evaluate our contributions on benchmarks for dense anomaly detection and open-set segmentation of traffic scenes. The experiments reveal strong open-set performance in spite of negligible computational overhead

Peephole optimization of asynchronous macromodule networks

Journal ArticleAbstract- Most high-level synthesis tools for asynchronous circuits take descriptions in concurrent hardware description languages and generate networks of macromodules or handshake components. In this paper, we propose a peephole optimizer for these networks. Our peephole optimizer first deduces an equivalent blackbox behavior for the network using Dill's tracetheoretic parallel composition operator. It then applies a new procedure called burst-mode reduction to obtain burst-mode machines from the deduced behavior. In a significant number of examples, our optimizer achieves gate-count improvements by a factor of five, and speed (cycle-time) improvements by a factor of two. Burst-mode reduction can be applied to any macromodule network that is delay insensitive as well as deterministic. A significant number of asynchronous circuits, especially those generated by asynchronous high-level synthesis tools, fall into this class, thus making our procedure widely applicable

A computational framework for sound segregation in music signals

Tese de doutoramento. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 200

Natural image processing and synthesis using deep learning

Nous étudions dans cette thèse comment les réseaux de neurones profonds peuvent être utilisés dans différents domaines de la vision artificielle. La vision artificielle est un domaine interdisciplinaire qui traite de la compréhension d’images et de vidéos numériques. Les problèmes de ce domaine ont traditionnellement été adressés avec des méthodes ad-hoc nécessitant beaucoup de réglages manuels. En effet, ces systèmes de vision artificiels comprenaient jusqu’à récemment une série de modules optimisés indépendamment. Cette approche est très raisonnable dans la mesure où, avec peu de données, elle bénéficient autant que possible des connaissances du chercheur. Mais cette avantage peut se révéler être une limitation si certaines données d’entré n’ont pas été considérées dans la conception de l’algorithme. Avec des volumes et une diversité de données toujours plus grands, ainsi que des capacités de calcul plus rapides et économiques, les réseaux de neurones profonds optimisés d’un bout à l’autre sont devenus une alternative attrayante. Nous démontrons leur avantage avec une série d’articles de recherche, chacun d’entre eux trouvant une solution à base de réseaux de neurones profonds à un problème d’analyse ou de synthèse visuelle particulier. Dans le premier article, nous considérons un problème de vision classique: la détection de bords et de contours. Nous partons de l’approche classique et la rendons plus ‘neurale’ en combinant deux étapes, la détection et la description de motifs visuels, en un seul réseau convolutionnel. Cette méthode, qui peut ainsi s’adapter à de nouveaux ensembles de données, s’avère être au moins aussi précis que les méthodes conventionnelles quand il s’agit de domaines qui leur sont favorables, tout en étant beaucoup plus robuste dans des domaines plus générales. Dans le deuxième article, nous construisons une nouvelle architecture pour la manipulation d’images qui utilise l’idée que la majorité des pixels produits peuvent d’être copiés de l’image d’entrée. Cette technique bénéficie de plusieurs avantages majeurs par rapport à l’approche conventionnelle en apprentissage profond. En effet, elle conserve les détails de l’image d’origine, n’introduit pas d’aberrations grâce à la capacité limitée du réseau sous-jacent et simplifie l’apprentissage. Nous démontrons l’efficacité de cette architecture dans le cadre d’une tâche de correction du regard, où notre système produit d’excellents résultats. Dans le troisième article, nous nous éclipsons de la vision artificielle pour étudier le problème plus générale de l’adaptation à de nouveaux domaines. Nous développons un nouvel algorithme d’apprentissage, qui assure l’adaptation avec un objectif auxiliaire à la tâche principale. Nous cherchons ainsi à extraire des motifs qui permettent d’accomplir la tâche mais qui ne permettent pas à un réseau dédié de reconnaître le domaine. Ce réseau est optimisé de manière simultané avec les motifs en question, et a pour tâche de reconnaître le domaine de provenance des motifs. Cette technique est simple à implémenter, et conduit pourtant à l’état de l’art sur toutes les tâches de référence. Enfin, le quatrième article présente un nouveau type de modèle génératif d’images. À l’opposé des approches conventionnels à base de réseaux de neurones convolutionnels, notre système baptisé SPIRAL décrit les images en termes de programmes bas-niveau qui sont exécutés par un logiciel de graphisme ordinaire. Entre autres, ceci permet à l’algorithme de ne pas s’attarder sur les détails de l’image, et de se concentrer plutôt sur sa structure globale. L’espace latent de notre modèle est, par construction, interprétable et permet de manipuler des images de façon prévisible. Nous montrons la capacité et l’agilité de cette approche sur plusieurs bases de données de référence.In the present thesis, we study how deep neural networks can be applied to various tasks in computer vision. Computer vision is an interdisciplinary field that deals with understanding of digital images and video. Traditionally, the problems arising in this domain were tackled using heavily hand-engineered adhoc methods. A typical computer vision system up until recently consisted of a sequence of independent modules which barely talked to each other. Such an approach is quite reasonable in the case of limited data as it takes major advantage of the researcher's domain expertise. This strength turns into a weakness if some of the input scenarios are overlooked in the algorithm design process. With the rapidly increasing volumes and varieties of data and the advent of cheaper and faster computational resources end-to-end deep neural networks have become an appealing alternative to the traditional computer vision pipelines. We demonstrate this in a series of research articles, each of which considers a particular task of either image analysis or synthesis and presenting a solution based on a ``deep'' backbone. In the first article, we deal with a classic low-level vision problem of edge detection. Inspired by a top-performing non-neural approach, we take a step towards building an end-to-end system by combining feature extraction and description in a single convolutional network. The resulting fully data-driven method matches or surpasses the detection quality of the existing conventional approaches in the settings for which they were designed while being significantly more usable in the out-of-domain situations. In our second article, we introduce a custom architecture for image manipulation based on the idea that most of the pixels in the output image can be directly copied from the input. This technique bears several significant advantages over the naive black-box neural approach. It retains the level of detail of the original images, does not introduce artifacts due to insufficient capacity of the underlying neural network and simplifies training process, to name a few. We demonstrate the efficiency of the proposed architecture on the challenging gaze correction task where our system achieves excellent results. In the third article, we slightly diverge from pure computer vision and study a more general problem of domain adaption. There, we introduce a novel training-time algorithm (\ie, adaptation is attained by using an auxilliary objective in addition to the main one). We seek to extract features that maximally confuse a dedicated network called domain classifier while being useful for the task at hand. The domain classifier is learned simultaneosly with the features and attempts to tell whether those features are coming from the source or the target domain. The proposed technique is easy to implement, yet results in superior performance in all the standard benchmarks. Finally, the fourth article presents a new kind of generative model for image data. Unlike conventional neural network based approaches our system dubbed SPIRAL describes images in terms of concise low-level programs executed by off-the-shelf rendering software used by humans to create visual content. Among other things, this allows SPIRAL not to waste its capacity on minutae of datasets and focus more on the global structure. The latent space of our model is easily interpretable by design and provides means for predictable image manipulation. We test our approach on several popular datasets and demonstrate its power and flexibility

Covering conditions and algorithms for the synthesis of speed-independent circuits

Journal ArticleAbstract-This paper presents theory and algorithms for the synthesis of standard C-implementations of speed-independent circuits. These implementations are block-level circuits which may consist of atomic gates to perform complex functions in order to ensure hazard freedom. First, we present Boolean covering conditions that guarantee that the standard C-implementations operate correctly. Then, we present two algorithms that produce optimal solutions to the covering problem. The first algorithm is always applicable, but does not complete on large circuits. The second algorithm, motivated by our observation that our covering problem can often be solved with a single cube, finds the optimal single-cube solution when such a solution exists. When applicable, the second algorithm is dramatically more efficient than the first, more general algorithm. We present results for benchmark specifications which indicate that our single-cube algorithm is applicable on most benchmark circuits and reduces run times by over an order of magnitude. The block-level circuits generated by our algorithms are a good starting point for tools that perform technology mapping to obtain gate-level speed independent circuits