Computation of Boolean Formulas Using Sneak Paths in Crossbar Computing

Memristor-based nano-crossbar computing is a revolutionary computing paradigm that does away with the traditional Von Neumann architectural separation of memory and computation units. The computation of Boolean formulas using memristor circuits has been a subject of several recent investigations. Crossbar computing, in general, has also been a topic of active interest, but sneak paths have posed a hurdle in the design of pervasive general-purpose crossbar computing paradigms. In this paper, we demonstrate that sneak paths in nano-crossbar computing can be exploited to design a Boolean-formula evaluation strategy. We demonstrate our approach on a simple Boolean formula and a 1-bit addition circuit. We also conjecture that our nano-crossbar design will be an effective approach for synthesizing high-performance customized arithmetic and logic circuits

In-Memory Computing Using Formal Methods and Paths-Based Logic

The continued scaling of the CMOS device has been largely responsible for the increase in computational power and consequent technological progress over the last few decades. However, the end of Dennard scaling has interrupted this era of sustained exponential growth in computing performance. Indeed, we are quickly reaching an impasse in the form of limitations in the lithographic processes used to fabricate CMOS processes and, even more dire, we are beginning to face fundamental physical phenomena, such as quantum tunneling, that are pervasive at the nanometer scale. Such phenomena manifests itself in prohibitively high leakage currents and process variations, leading to inaccurate computations. As a result, there has been a surge of interest in computing architectures that can replace the traditional CMOS transistor-based methods. This thesis is a thorough investigation of how computations can be performed on one such architecture, called a crossbar. The methods proposed in this document apply to any crossbar consisting of two-terminal connective devices. First, we demonstrate how paths of electric current between two wires can be used as design primitives in a crossbar. We then leverage principles from the field of formal methods, in particular the area of bounded model checking, to automate the synthesis of crossbar designs for computing arithmetic operations. We demonstrate that our approach yields circuits that are state-of-the-art in terms of the number of operations required to perform a computation. Finally, we look at the benefits of using a 3D crossbar for computation; that is, a crossbar consisting of multiple layers of interconnects. A novel 3D crossbar computing paradigm is proposed for solving the Boolean matrix multiplication and transitive closure problems and we show how this paradigm can be utilized, with small modifications, in the XPoint crossbar memory architecture that was recently announced by Intel

BOSS: Bidirectional One-Shot Synthesis of Adversarial Examples

The design of additive imperceptible perturbations to the inputs of deep classifiers to maximize their misclassification rates is a central focus of adversarial machine learning. An alternative approach is to synthesize adversarial examples from scratch using GAN-like structures, albeit with the use of large amounts of training data. By contrast, this paper considers one-shot synthesis of adversarial examples; the inputs are synthesized from scratch to induce arbitrary soft predictions at the output of pre-trained models, while simultaneously maintaining high similarity to specified inputs. To this end, we present a problem that encodes objectives on the distance between the desired and output distributions of the trained model and the similarity between such inputs and the synthesized examples. We prove that the formulated problem is NP-complete. Then, we advance a generative approach to the solution in which the adversarial examples are obtained as the output of a generative network whose parameters are iteratively updated by optimizing surrogate loss functions for the dual-objective. We demonstrate the generality and versatility of the framework and approach proposed through applications to the design of targeted adversarial attacks, generation of decision boundary samples, and synthesis of low confidence classification inputs. The approach is further extended to an ensemble of models with different soft output specifications. The experimental results verify that the targeted and confidence reduction attack methods developed perform on par with state-of-the-art algorithms

Diagnóstico, análisis y propuesta de mejora en la fabricación de semirremolques volquete en una empresa metalmecánica

El presente trabajo de tesis tiene como objetivo principal realizar un diagnóstico, un análisis y brindar propuestas de mejora para el proceso de fabricación de semirremolques volquetes en la Empresa metalmecánica en estudio. La tesis comprende la descripción de las herramientas de análisis y mejora a desarrollar, una descripción general de la Empresa, el diagnóstico y análisis del problema más relevante de la Empresa y sus causas potenciales, la implementación de las herramientas de mejora propuestas y finalmente la factibilidad económica de la implementación de las propuestas de mejora. A partir del diagnóstico de la Empresa, se identificó que el problema más relevante que afecta a la Empresa es el frecuente retraso en la entrega de las ordenes de pedido a los clientes. En función a ello, se buscó las causas posibles del problema mediante una lluvia de ideas proporcionada por el personal que trabaja directamente en la fabricación, se analizaron las posibles causas del problema mediante el diagrama de Ishikawa y diagrama de Pareto, concluyendo que las causas principales son las siguientes: una ausencia de orden y limpieza en las áreas de trabajo; falta de organización de herramientas, planos y artículos en las mesas de trabajo; demora en el subproceso de habilitado de material, subproceso crítico de la planta; Falta de comunicación entre las áreas de ingeniería; y una ineficaz coordinación entre el Área de ventas y producción para establecer la fecha de entrega de las ordenes de pedido. Por lo tanto se plantea la primer propuesta de mejora, metodología 5S, para tener áreas de trabajo más ordenadas y limpias que permitan un flujo productivo continuo mediante la mejora en la utilización de del subproceso crítico, subproceso de habilitado, de 88.43% a 89.57% reduciendo la cadencia de 152.67 a 150.73 minutos por unidad y aumentando la capacidad de planta de 75 a 76 unidades mensuales. Como segunda propuesta se plantea un programa de comunicación y coordinación interna empleando Gestión de la Calidad Total (TQM) para mejorar la comunicación entre las Áreas de Producción, Ingeniería y Ventas, ya sea al momento de establecer la fecha de entrega de un pedido con el Área de Ventas o cuando se haya realizado una modificación en los planos de la unidad con el Área de Ingeniería y así minimizar la cantidad de unidades defectuosas anuales. Y como última propuesta se aplica la teoría de restricciones (TOC) la cual tiene como objetivo determinar el subproceso crítico, que en el caso de la Empresa es el subproceso de habilitado, para luego explotar este subproceso y mejorar su tiempo de ciclo o cadencia de 150.73 a 145.83 minutos por unidad y por consecuencia mejorar la capacidad de producción de 76 a 78 unidades mensuales igualando la demanda promedio mensual del año 2017. Luego el ritmo de producción de los subprocesos de fabricación irá al ritmo del subproceso critico mediante el uso del sistema tambor-amortiguador-cuerda. Finalmente, en el caso que el promedio de unidades demandadas aumente, ya que la Empresa trabaja con un sistema de producción PULL, se procederá a elevar la restricción con la implementación de un segundo turno para el Área de habilitado. Finalmente, se considera un horizonte de proyecto de cinco años con una inversión total de S/. 202,960, con lo cual se obtiene un beneficio total de S/. 1, 897,231 y un ratio Beneficio-costo de 9.348. Estos indicadores reflejan que el proyecto es viable económicamente, ya que los beneficios son mayores a la inversión y el ratio beneficio-costo es mayor a uno.Tesi

Automaton-Guided Curriculum Generation for Reinforcement Learning Agents

Despite advances in Reinforcement Learning, many sequential decision making tasks remain prohibitively expensive and impractical to learn. Recently, approaches that automatically generate reward functions from logical task specifications have been proposed to mitigate this issue; however, they scale poorly on long-horizon tasks (i.e., tasks where the agent needs to perform a series of correct actions to reach the goal state, considering future transitions while choosing an action). Employing a curriculum (a sequence of increasingly complex tasks) further improves the learning speed of the agent by sequencing intermediate tasks suited to the learning capacity of the agent. However, generating curricula from the logical specification still remains an unsolved problem. To this end, we propose AGCL, Automaton-guided Curriculum Learning, a novel method for automatically generating curricula for the target task in the form of Directed Acyclic Graphs (DAGs). AGCL encodes the specification in the form of a deterministic finite automaton (DFA), and then uses the DFA along with the Object-Oriented MDP (OOMDP) representation to generate a curriculum as a DAG, where the vertices correspond to tasks, and edges correspond to the direction of knowledge transfer. Experiments in gridworld and physics-based simulated robotics domains show that the curricula produced by AGCL achieve improved time-to-threshold performance on a complex sequential decision-making problem relative to state-of-the-art curriculum learning (e.g, teacher-student, self-play) and automaton-guided reinforcement learning baselines (e.g, Q-Learning for Reward Machines). Further, we demonstrate that AGCL performs well even in the presence of noise in the task's OOMDP description, and also when distractor objects are present that are not modeled in the logical specification of the tasks' objectives.Comment: To be presented at The International Conference on Automated Planning and Scheduling (ICAPS) 202

Automaton Distillation: Neuro-Symbolic Transfer Learning for Deep Reinforcement Learning

Reinforcement learning (RL) is a powerful tool for finding optimal policies in sequential decision processes. However, deep RL methods suffer from two weaknesses: collecting the amount of agent experience required for practical RL problems is prohibitively expensive, and the learned policies exhibit poor generalization on tasks outside of the training distribution. To mitigate these issues, we introduce automaton distillation, a form of neuro-symbolic transfer learning in which Q-value estimates from a teacher are distilled into a low-dimensional representation in the form of an automaton. We then propose two methods for generating Q-value estimates: static transfer, which reasons over an abstract Markov Decision Process constructed based on prior knowledge, and dynamic transfer, where symbolic information is extracted from a teacher Deep Q-Network (DQN). The resulting Q-value estimates from either method are used to bootstrap learning in the target environment via a modified DQN loss function. We list several failure modes of existing automaton-based transfer methods and demonstrate that both static and dynamic automaton distillation decrease the time required to find optimal policies for various decision tasks