An overview of decision table literature.

The present report contains an overview of the literature on decision tables since its origin. The goal is to analyze the dissemination of decision tables in different areas of knowledge, countries and languages, especially showing these that present the most interest on decision table use. In the first part a description of the scope of the overview is given. Next, the classification results by topic are explained. An abstract and some keywords are included for each reference, normally provided by the authors. In some cases own comments are added. The purpose of these comments is to show where, how and why decision tables are used. Other examined topics are the theoretical or practical feature of each document, as well as its origin country and language. Finally, the main body of the paper consists of the ordered list of publications with abstract, classification and comments.

Survey on Instruction Selection: An Extensive and Modern Literature Review

Instruction selection is one of three optimisation problems involved in the code generator backend of a compiler. The instruction selector is responsible of transforming an input program from its target-independent representation into a target-specific form by making best use of the available machine instructions. Hence instruction selection is a crucial part of efficient code generation. Despite on-going research since the late 1960s, the last, comprehensive survey on the field was written more than 30 years ago. As new approaches and techniques have appeared since its publication, this brings forth a need for a new, up-to-date review of the current body of literature. This report addresses that need by performing an extensive review and categorisation of existing research. The report therefore supersedes and extends the previous surveys, and also attempts to identify where future research should be directed.Comment: Major changes: - Merged simulation chapter with macro expansion chapter - Addressed misunderstandings of several approaches - Completely rewrote many parts of the chapters; strengthened the discussion of many approaches - Revised the drawing of all trees and graphs to put the root at the top instead of at the bottom - Added appendix for listing the approaches in a table See doc for more inf

Hybrid Analog-Digital Co-Processing for Scientific Computation

In the past 10 years computer architecture research has moved to more heterogeneity and less adherence to conventional abstractions. Scientists and engineers hold an unshakable belief that computing holds keys to unlocking humanity's Grand Challenges. Acting on that belief they have looked deeper into computer architecture to find specialized support for their applications. Likewise, computer architects have looked deeper into circuits and devices in search of untapped performance and efficiency. The lines between computer architecture layers---applications, algorithms, architectures, microarchitectures, circuits and devices---have blurred. Against this backdrop, a menagerie of computer architectures are on the horizon, ones that forgo basic assumptions about computer hardware, and require new thinking of how such hardware supports problems and algorithms. This thesis is about revisiting hybrid analog-digital computing in support of diverse modern workloads. Hybrid computing had extensive applications in early computing history, and has been revisited for small-scale applications in embedded systems. But architectural support for using hybrid computing in modern workloads, at scale and with high accuracy solutions, has been lacking. I demonstrate solving a variety of scientific computing problems, including stochastic ODEs, partial differential equations, linear algebra, and nonlinear systems of equations, as case studies in hybrid computing. I solve these problems on a system of multiple prototype analog accelerator chips built by a team at Columbia University. On that team I made contributions toward programming the chips, building the digital interface, and validating the chips' functionality. The analog accelerator chip is intended for use in conjunction with a conventional digital host computer. The appeal and motivation for using an analog accelerator is efficiency and performance, but it comes with limitations in accuracy and problem sizes that we have to work around. The first problem is how to do problems in this unconventional computation model. Scientific computing phrases problems as differential equations and algebraic equations. Differential equations are a continuous view of the world, while algebraic equations are a discrete one. Prior work in analog computing mostly focused on differential equations; algebraic equations played a minor role in prior work in analog computing. The secret to using the analog accelerator to support modern workloads on conventional computers is that these two viewpoints are interchangeable. The algebraic equations that underlie most workloads can be solved as differential equations, and differential equations are naturally solvable in the analog accelerator chip. A hybrid analog-digital computer architecture can focus on solving linear and nonlinear algebra problems to support many workloads. The second problem is how to get accurate solutions using hybrid analog-digital computing. The reason that the analog computation model gives less accurate solutions is it gives up representing numbers as digital binary numbers, and instead uses the full range of analog voltage and current to represent real numbers. Prior work has established that encoding data in analog signals gives an energy efficiency advantage as long as the analog data precision is limited. While the analog accelerator alone may be useful for energy-constrained applications where inputs and outputs are imprecise, we are more interested in using analog in conjunction with digital for precise solutions. This thesis gives novel insight that the trick to do so is to solve nonlinear problems where low-precision guesses are useful for conventional digital algorithms. The third problem is how to solve large problems using hybrid analog-digital computing. The reason the analog computation model can't handle large problems is it gives up step-by-step discrete-time operation, instead allowing variables to evolve smoothly in continuous time. To make that happen the analog accelerator works by chaining hardware for mathematical operations end-to-end. During computation analog data flows through the hardware with no overheads in control logic and memory accesses. The downside is then the needed hardware size grows alongside problem sizes. While scientific computing researchers have for a long time split large problems into smaller subproblems to fit in digital computer constraints, this thesis is a first attempt to consider these divide-and-conquer algorithms as an essential tool in using the analog model of computation. As we enter the post-Moore’s law era of computing, unconventional architectures will offer specialized models of computation that uniquely support specific problem types. Two prominent examples are deep neural networks and quantum computers. Recent trends in computer science research show these unconventional architectures will soon have broad adoption. In this thesis I show another specialized, unconventional architecture is to use analog accelerators to solve problems in scientific computing. Computer architecture researchers will discover other important models of computation in the future. This thesis is an example of the discovery process, implementation, and evaluation of how an unconventional architecture supports specialized workloads

Management. A continuing bibliography with indexes

This bibliography cites 604 reports, articles, and other documents introduced into the NASA scientific and technical information system in 1979 covering the management of research and development, contracts, production, logistics, personnel, safety, reliability and quality control. Program, project, and systems management; management policy, philosophy, tools, and techniques; decision making processes for managers; technology assessment; management of urban problems; and information for managers on Federal resources, expenditures, financing, and budgeting are also covered. Abstracts are provided as well as subject, personal author, and corporate source indexes

Enabling caches in probabilistic timing analysis

Hardware and software complexity of future critical real-time systems challenges the scalability of traditional timing analysis methods. Measurement-Based Probabilistic Timing Analysis (MBPTA) has recently emerged as an industrially-viable alternative technique to deal with complex hardware/software. Yet, MBPTA requires certain timing properties in the system under analysis that are not satisfied in conventional systems. In this thesis, we introduce, for the first time, hardware and software solutions to satisfy those requirements as well as to improve MBPTA applicability. We focus on one of the hardware resources with highest impact on both average performance and Worst-Case Execution Time (WCET) in current real-time platforms, the cache. In this line, the contributions of this thesis follow three different axes: hardware solutions and software solutions to enable MBPTA, and MBPTA analysis enhancements in systems featuring caches. At hardware level, we set the foundations of MBPTA-compliant processor designs, and define efficient time-randomised cache designs for single- and multi-level hierarchies of arbitrary complexity, including unified caches, which can be time-analysed for the first time. We propose three new software randomisation approaches (one dynamic and two static variants) to control, in an MBPTA-compliant manner, the cache jitter in Commercial off-the-shelf (COTS) processors in real-time systems. To that end, all variants randomly vary the location of programs' code and data in memory across runs, to achieve probabilistic timing properties similar to those achieved with customised hardware cache designs. We propose a novel method to estimate the WCET of a program using MBPTA, without requiring the end-user to identify worst-case paths and inputs, improving its applicability in industry. We also introduce Probabilistic Timing Composability, which allows Integrated Systems to reduce their WCET in the presence of time-randomised caches. With the above contributions, this thesis pushes the limits in the use of complex real-time embedded processor designs equipped with caches and paves the way towards the industrialisation of MBPTA technology.La complejidad de hardware y software de los sistemas críticos del futuro desafía la escalabilidad de los métodos tradicionales de análisis temporal. El análisis temporal probabilístico basado en medidas (MBPTA) ha aparecido últimamente como una solución viable alternativa para la industria, para manejar hardware/software complejo. Sin embargo, MBPTA requiere ciertas propiedades de tiempo en el sistema bajo análisis que no satisfacen los sistemas convencionales. En esta tesis introducimos, por primera vez, soluciones hardware y software para satisfacer estos requisitos como también mejorar la aplicabilidad de MBPTA. Nos centramos en uno de los recursos hardware con el máximo impacto en el rendimiento medio y el peor caso del tiempo de ejecución (WCET) en plataformas actuales de tiempo real, la cache. En esta línea, las contribuciones de esta tesis siguen 3 ejes distintos: soluciones hardware y soluciones software para habilitar MBPTA, y mejoras de el análisis MBPTA en sistemas usado caches. A nivel de hardware, creamos las bases del diseño de un procesador compatible con MBPTA, y definimos diseños de cache con tiempo aleatorio para jerarquías de memoria con uno y múltiples niveles de cualquier complejidad, incluso caches unificadas, las cuales pueden ser analizadas temporalmente por primera vez. Proponemos tres nuevos enfoques de aleatorización de software (uno dinámico y dos variedades estáticas) para manejar, en una manera compatible con MBPTA, la variabilidad del tiempo (jitter) de la cache en procesadores comerciales comunes en el mercado (COTS) en sistemas de tiempo real. Por eso, todas nuestras propuestas varían aleatoriamente la posición del código y de los datos del programa en la memoria entre ejecuciones del mismo, para conseguir propiedades de tiempo aleatorias, similares a las logradas con diseños hardware personalizados. Proponemos un nuevo método para estimar el WCET de un programa usando MBPTA, sin requerir que el usuario dentifique los caminos y las entradas de programa del peor caso, mejorando así la aplicabilidad de MBPTA en la industria. Además, introducimos la composabilidad de tiempo probabilística, que permite a los sistemas integrados reducir su WCET cuando usan caches de tiempo aleatorio. Con estas contribuciones, esta tesis empuja los limites en el uso de diseños complejos de procesadores empotrados en sistemas de tiempo real equipados con caches y prepara el terreno para la industrialización de la tecnología MBPTA

Efficient hardware implementations of bio-inspired networks

The human brain, with its massive computational capability and power efficiency in small form factor, continues to inspire the ultimate goal of building machines that can perform tasks without being explicitly programmed. In an effort to mimic the natural information processing paradigms observed in the brain, several neural network generations have been proposed over the years. Among the neural networks inspired by biology, second-generation Artificial or Deep Neural Networks (ANNs/DNNs) use memoryless neuron models and have shown unprecedented success surpassing humans in a wide variety of tasks. Unlike ANNs, third-generation Spiking Neural Networks (SNNs) closely mimic biological neurons by operating on discrete and sparse events in time called spikes, which are obtained by the time integration of previous inputs. Implementation of data-intensive neural network models on computers based on the von Neumann architecture is mainly limited by the continuous data transfer between the physically separated memory and processing units. Hence, non-von Neumann architectural solutions are essential for processing these memory-intensive bio-inspired neural networks in an energy-efficient manner. Among the non-von Neumann architectures, implementations employing non-volatile memory (NVM) devices are most promising due to their compact size and low operating power. However, it is non-trivial to integrate these nanoscale devices on conventional computational substrates due to their non-idealities, such as limited dynamic range, finite bit resolution, programming variability, etc. This dissertation demonstrates the architectural and algorithmic optimizations of implementing bio-inspired neural networks using emerging nanoscale devices. The first half of the dissertation focuses on the hardware acceleration of DNN implementations. A 4-layer stochastic DNN in a crossbar architecture with memristive devices at the cross point is analyzed for accelerating DNN training. This network is then used as a baseline to explore the impact of experimental memristive device behavior on network performance. Programming variability is found to have a critical role in determining network performance compared to other non-ideal characteristics of the devices. In addition, noise-resilient inference engines are demonstrated using stochastic memristive DNNs with 100 bits for stochastic encoding during inference and 10 bits for the expensive training. The second half of the dissertation focuses on a novel probabilistic framework for SNNs using the Generalized Linear Model (GLM) neurons for capturing neuronal behavior. This work demonstrates that probabilistic SNNs have comparable perform-ance against equivalent ANNs on two popular benchmarks - handwritten-digit classification and human activity recognition. Considering the potential of SNNs in energy-efficient implementations, a hardware accelerator for inference is proposed, termed as Spintronic Accelerator for Probabilistic SNNs (SpinAPS). The learning algorithm is optimized for a hardware friendly implementation and uses first-to-spike decoding scheme for low latency inference. With binary spintronic synapses and digital CMOS logic neurons for computations, SpinAPS achieves a performance improvement of 4x in terms of GSOPS/W/mm$^2$ when compared to a conventional SRAM-based design. Collectively, this work demonstrates the potential of emerging memory technologies in building energy-efficient hardware architectures for deep and spiking neural networks. The design strategies adopted in this work can be extended to other spike and non-spike based systems for building embedded solutions having power/energy constraints

High-Density Solid-State Memory Devices and Technologies

This Special Issue aims to examine high-density solid-state memory devices and technologies from various standpoints in an attempt to foster their continuous success in the future. Considering that broadening of the range of applications will likely offer different types of solid-state memories their chance in the spotlight, the Special Issue is not focused on a specific storage solution but rather embraces all the most relevant solid-state memory devices and technologies currently on stage. Even the subjects dealt with in this Special Issue are widespread, ranging from process and design issues/innovations to the experimental and theoretical analysis of the operation and from the performance and reliability of memory devices and arrays to the exploitation of solid-state memories to pursue new computing paradigms

Defense in Depth of Resource-Constrained Devices

The emergent next generation of computing, the so-called Internet of Things (IoT), presents significant challenges to security, privacy, and trust. The devices commonly used in IoT scenarios are often resource-constrained with reduced computational strength, limited power consumption, and stringent availability requirements. Additionally, at least in the consumer arena, time-to-market is often prioritized at the expense of quality assurance and security. An initial lack of standards has compounded the problems arising from this rapid development. However, the explosive growth in the number and types of IoT devices has now created a multitude of competing standards and technology silos resulting in a highly fragmented threat model. Tens of billions of these devices have been deployed in consumers\u27 homes and industrial settings. From smart toasters and personal health monitors to industrial controls in energy delivery networks, these devices wield significant influence on our daily lives. They are privy to highly sensitive, often personal data and responsible for real-world, security-critical, physical processes. As such, these internet-connected things are highly valuable and vulnerable targets for exploitation. Current security measures, such as reactionary policies and ad hoc patching, are not adequate at this scale. This thesis presents a multi-layered, defense in depth, approach to preventing and mitigating a myriad of vulnerabilities associated with the above challenges. To secure the pre-boot environment, we demonstrate a hardware-based secure boot process for devices lacking secure memory. We introduce a novel implementation of remote attestation backed by blockchain technologies to address hardware and software integrity concerns for the long-running, unsupervised, and rarely patched systems found in industrial IoT settings. Moving into the software layer, we present a unique method of intraprocess memory isolation as a barrier to several prevalent classes of software vulnerabilities. Finally, we exhibit work on network analysis and intrusion detection for the low-power, low-latency, and low-bandwidth wireless networks common to IoT applications. By targeting these areas of the hardware-software stack, we seek to establish a trustworthy system that extends from power-on through application runtime