Variability-Aware Design of Multilevel Logic Decoders for Nanoscale Crossbar Memories

The fabrication of crossbar memories with sublithographic features is expected to be feasible within several emerging technologies; in all of them, the nanowire (NW) decoder is a critical part since it bridges the sublithographic wires to the outer circuitry that is defined on the lithography scale. In this paper, we evaluate the addressing scheme of the decoder circuit for NW crossbar arrays, based on the existing technological solutions for threshold voltage differentiation of NW devices. This is equivalent to using a multivalued logic addressing scheme. With this approach, it is possible to reduce the decoder size and keep it defect tolerant. We formally define two types of multivalued codes (i.e., hot and reflexive codes), and we estimate their yield under high variability conditions. Multivalued hot decoders yield better area saving than n-ary reflexive codes, and under severe conditions, reflexive codes enable a nonvanishing part of the code space to randomly recover. The choice of the optimal combination of decoder type and logic level saves area up to 24%. We also show that the precision of the addressing voltages when a high variability affects the threshold voltages is a crucial parameter for the decoder design and permits large savings in memory area. Moreover, a precise knowledge about the variability level improves the design of memory decoders by giving the right optimal code

The implementation and applications of multiple-valued logic

Multiple-Valued Logic (MVL) takes two major forms. Multiple-valued circuits can implement the logic directly by using multiple-valued signals, or the logic can be implemented indirectly with binary circuits, by using more than one binary signal to represent a single multiple-valued signal. Techniques such as carry-save addition can be viewed as indirectly implemented MVL. Both direct and indirect techniques have been shown in the past to provide advantages over conventional arithmetic and logic techniques in algorithms required widely in computing for applications such as image and signal processing. It is possible to implement basic MVL building blocks at the transistor level. However, these circuits are difficult to design due to their non binary nature. In the design stage they are more like analogue circuits than binary circuits. Current integrated circuit technologies are biased towards binary circuitry. However, in spite of this, there is potential for power and area savings from MVL circuits, especially in technologies such as BiCMOS. This thesis shows that the use of voltage mode MVL will, in general not provide bandwidth increases on circuit buses because the buses become slower as the number of signal levels increases. Current mode MVL circuits however do have potential to reduce power and area requirements of arithmetic circuitry. The design of transistor level circuits is investigated in terms of a modern production technology. A novel methodology for the design of current mode MVL circuits is developed. The methodology is based upon the novel concept of the use of non-linear current encoding of signals, providing the opportunity for the efficient design of many previously unimplemented circuits in current mode MVL. This methodology is used to design a useful set of basic MVL building blocks, and fabrication results are reported. The creation of libraries of MVL circuits is also discussed. The CORDIC algorithm for two dimensional vector rotation is examined in detail as an example for indirect MVL implementation. The algorithm is extended to a set of three dimensional vector rotators using conventional arithmetic, redundant radix four arithmetic, and Taylor's series expansions. These algorithms can be used for two dimensional vector rotations in which no scale factor corrections are needed. The new algorithms are compared in terms of basic VLSI criteria against previously reported algorithms. A pipelined version of the redundant arithmetic algorithm is floorplanned and partially laid out to give indications of wiring overheads, and layout densities. An indirectly implemented MVL algorithm such as the CORDIC algorithm described in this thesis would clearly benefit from direct implementation in MVL

Binary RDF for Scalable Publishing, Exchanging and Consumption in the Web of Data

El actual diluvio de datos está inundando la web con grandes volúmenes de datos representados en RDF, dando lugar a la denominada 'Web de Datos'. En esta tesis proponemos, en primer lugar, un estudio profundo de aquellos textos que nos permitan abordar un conocimiento global de la estructura real de los conjuntos de datos RDF, HDT, que afronta la representación eficiente de grandes volúmenes de datos RDF a través de estructuras optimizadas para su almacenamiento y transmisión en red. HDT representa efizcamente un conjunto de datos RDF a través de su división en tres componentes: la cabecera (Header), el diccionario (Dictionary) y la estructura de sentencias RDF (Triples). A continuación, nos centramos en proveer estructuras eficientes de dichos componentes, ocupando un espacio comprimido al tiempo que se permite el acceso directo a cualquier dat

NASA SERC 1990 Symposium on VLSI Design

This document contains papers presented at the first annual NASA Symposium on VLSI Design. NASA's involvement in this event demonstrates a need for research and development in high performance computing. High performance computing addresses problems faced by the scientific and industrial communities. High performance computing is needed in: (1) real-time manipulation of large data sets; (2) advanced systems control of spacecraft; (3) digital data transmission, error correction, and image compression; and (4) expert system control of spacecraft. Clearly, a valuable technology in meeting these needs is Very Large Scale Integration (VLSI). This conference addresses the following issues in VLSI design: (1) system architectures; (2) electronics; (3) algorithms; and (4) CAD tools

Deep Learning Approaches to Goal Recognition

Riconoscere il goal di un agente utilizzando una traccia di osservazioni è un compito importante con diverse applicazioni. In letteratura, molti approcci di goal recognition (GR) si basano sull'applicazione di tecniche di pianificazione automatica che richiedono un modello delle azioni del dominio e dello stato iniziale del dominio (scritto, ad esempio, in PDDL). In questa tesi studiamo tre approcci alternativi (GRNet, Fast and Slow Goal Recognition e un approccio basato su BERT) in cui il goal recognition è formulato come un compito di classificazione affrontato utilizzando il machine learning. Tutti questi approcci mirano principalmente a risolvere istanze di GR in un dato dominio, specificato da un insieme di proposizioni e da un insieme di nomi di azioni. In GRNet, le istanze di classificazione del dominio sono risolte da una rete LSTM. L'unica informazione richiesta come input della rete addestrata è una traccia di nomi di azioni, ognuno dei quali indica solo il nome di un'azione osservata. Un'esecuzione della LSTM elabora una traccia di azioni osservate per calcolare la probabilità che ogni proposizione del dominio faccia parte del goal dell'agente. Fast and Slow Goal Recognition, ispirato al framework ``Thinking Fast and Slow'', è un modello a doppio processo che integra l'uso delle sopra-citate reti LSTM con le tecniche di pianificazione automatica. Questa architettura può sfruttare sia il riconoscimento veloce dei goal, basato sull'esperienza, fornito dalla rete, sia l'analisi lenta e deliberata fornita dalle tecniche di pianificazione. Infine, studiamo come un modello BERT addestrato sui piani sia in grado di comprendere il funzionamento di un dominio, le sue azioni e le loro relazioni reciproche. Questo modello viene poi sottoposto a fine-tuning per classificare le istanze di goal recognition. Le analisi sperimentali confermano che le architetture presentate raggiungono buone prestazioni sia in termini di accuratezza della classificazione dei goal che di tempo di esecuzione, ottenendo spesso risultati migliori rispetto a un sistema di goal recognition allo stato dell'arte sui benchmark considerati.Recognising the goal of an agent from a trace of observations is an important task with many applications. In the literature, many approaches to goal recognition (GR) rely on the application of automated planning techniques which requires a model of the domain actions and of the initial domain state (written, e.g., in PDDL). We study three alternative approaches (GRNet, Fast and Slow Goal Recognition and a BERT-based approach) where Goal Recognition is formulated as a classification task addressed by machine learning. All these approaches are primarily aimed at solving GR instances in a given domain, which is specified by a set of propositions and a set of action names. In GRNet, the goal classification instances in the domain are solved by an LSTM network. The only information required as input of the trained network is a trace of action names, each one indicating just the name of an observed action. A run of the LSTM processes a trace of observed actions to compute how likely it is that each domain proposition is part of the agent's goal. Fast and Slow Goal Recognition, inspired by the ``Thinking Fast and Slow'' framework, is a dual-process model which integrates the use of the aforementioned LSTM with the automated planning techniques. This architecture can exploit both the fast, experience-based goal recognition provided by the network, and slow, deliberate analysis provided by the planning techniques. Finally, we study how a BERT model trained on plans is able to understand how a domain works, its actions and how they are related to each other. This model is then fine-tuned in order to classify goal recognition instances. Experimental analyses confirms that the presented architectures achieve good performance in terms of both goal classification accuracy and runtime, often obtaining better results w.r.t. a state-of-the-art GR system over the considered benchmarks

Learning understandable classifier models.

The topic of this dissertation is the automation of the process of extracting understandable patterns and rules from data. An unprecedented amount of data is available to anyone with a computer connected to the Internet. The disciplines of Data Mining and Machine Learning have emerged over the last two decades to face this challenge. This has led to the development of many tools and methods. These tools often produce models that make very accurate predictions about previously unseen data. However, models built by the most accurate methods are usually hard to understand or interpret by humans. In consequence, they deliver only decisions, and are short of any explanations. Hence they do not directly lead to the acquisition of new knowledge. This dissertation contributes to bridging the gap between the accurate opaque models and those less accurate but more transparent for humans. This dissertation first defines the problem of learning from data. It surveys the state-of-the-art methods for supervised learning of both understandable and opaque models from data, as well as unsupervised methods that detect features present in the data. It describes popular methods of rule extraction from unintelligible models which rewrite them into an understandable form. Limitations of rule extraction are described. A novel definition of understandability which ties computational complexity and learning is provided to show that rule extraction is an NP-hard problem. Next, a discussion whether one can expect that even an accurate classifier has learned new knowledge. The survey ends with a presentation of two approaches to building of understandable classifiers. On the one hand, understandable models must be able to accurately describe relations in the data. On the other hand, often a description of the output of a system in terms of its input requires the introduction of intermediate concepts, called features. Therefore it is crucial to develop methods that describe the data with understandable features and are able to use those features to present the relation that describes the data. Novel contributions of this thesis follow the survey. Two families of rule extraction algorithms are considered. First, a method that can work with any opaque classifier is introduced. Artificial training patterns are generated in a mathematically sound way and used to train more accurate understandable models. Subsequently, two novel algorithms that require that the opaque model is a Neural Network are presented. They rely on access to the network\u27s weights and biases to induce rules encoded as Decision Diagrams. Finally, the topic of feature extraction is considered. The impact on imposing non-negativity constraints on the weights of a neural network is considered. It is proved that a three layer network with non-negative weights can shatter any given set of points and experiments are conducted to assess the accuracy and interpretability of such networks. Then, a novel path-following algorithm that finds robust sparse encodings of data is presented. In summary, this dissertation contributes to improved understandability of classifiers in several tangible and original ways. It introduces three distinct aspects of achieving this goal: infusion of additional patterns from the underlying pattern distribution into rule learners, the derivation of decision diagrams from neural networks, and achieving sparse coding with neural networks with non-negative weights

The 1991 3rd NASA Symposium on VLSI Design

Papers from the symposium are presented from the following sessions: (1) featured presentations 1; (2) very large scale integration (VLSI) circuit design; (3) VLSI architecture 1; (4) featured presentations 2; (5) neural networks; (6) VLSI architectures 2; (7) featured presentations 3; (8) verification 1; (9) analog design; (10) verification 2; (11) design innovations 1; (12) asynchronous design; and (13) design innovations 2

Towards Formal Structural Representation of Spoken Language: An Evolving Transformation System (ETS) Approach

Speech recognition has been a very active area of research over the past twenty years. Despite an evident progress, it is generally agreed by the practitioners of the field that performance of the current speech recognition systems is rather suboptimal and new approaches are needed. The motivation behind the undertaken research is an observation that the notion of representation of objects and concepts that once was considered to be central in the early days of pattern recognition, has been largely marginalised by the advent of statistical approaches. As a consequence of a predominantly statistical approach to speech recognition problem, due to the numeric, feature vector-based, nature of representation, the classes inductively discovered from real data using decision-theoretic techniques have little meaning outside the statistical framework. This is because decision surfaces or probability distributions are difficult to analyse linguistically. Because of the later limitation it is doubtful that the gap between speech recognition and linguistic research can be bridged by the numeric representations. This thesis investigates an alternative, structural, approach to spoken language representation and categorisation. The approach pursued in this thesis is based on a consistent program, known as the Evolving Transformation System (ETS), motivated by the development and clarification of the concept of structural representation in pattern recognition and artificial intelligence from both theoretical and applied points of view. This thesis consists of two parts. In the first part of this thesis, a similarity-based approach to structural representation of speech is presented. First, a linguistically well-motivated structural representation of phones based on distinctive phonological features recovered from speech is proposed. The representation consists of string templates representing phones together with a similarity measure. The set of phonological templates together with a similarity measure defines a symbolic metric space. Representation and ETS-inspired categorisation in the symbolic metric spaces corresponding to the phonological structural representation are then investigated by constructing appropriate symbolic space classifiers and evaluating them on a standard corpus of read speech. In addition, similarity-based isometric transition from phonological symbolic metric spaces to the corresponding non-Euclidean vector spaces is investigated. Second part of this thesis deals with the formal approach to structural representation of spoken language. Unlike the approach adopted in the first part of this thesis, the representation developed in the second part is based on the mathematical language of the ETS formalism. This formalism has been specifically developed for structural modelling of dynamic processes. In particular, it allows the representation of both objects and classes in a uniform event-based hierarchical framework. In this thesis, the latter property of the formalism allows the adoption of a more physiologically-concreteapproach to structural representation. The proposed representation is based on gestural structures and encapsulates speech processes at the articulatory level. Algorithms for deriving the articulatory structures from the data are presented and evaluated

Applications of topology to Weyl semimetals and quantum computing

This thesis covers various applications of topology in condensed matter physics and quantum information. It studies how the topology of the electronic structure of a Weyl semimetal affects the transport behaviour of electrons in an applied magnetic field, and how one may employ similar ideas in materials containing Majorana modes to speed up chemistry calculations on a quantum computer. It develops and tests new techniques for decoding topological quantum error correcting codes, in particular for detailed simulation on near-term devices. Finally, it looks towards improving quantum algorithms for future applications in quantum simulation; in particular the classical post-processing of data taken during quantum phase estimation experiments.European Research Council; Netherlands Organization for Scientific ResearchQuantum Matter and Optic