Dynamic time management for improved accuracy and speed in host-compiled multi-core platform models

textWith increasing complexity and software content, modern embedded platforms employ a heterogeneous mix of multi-core processors along with hardware accelerators in order to provide high performance in limited power budgets. Due to complex interactions and highly dynamic behavior, static analysis of real-time performance and other constraints is challenging. As an alternative, full-system simulations have been widely accepted by designers. With traditional approaches being either slow or inaccurate, so-called host-compiled simulators have recently emerged as a solution for rapid evaluation of complete systems at early design stages. In such approaches, a faster simulation is achieved by natively executing application code at the source level, abstracting execution behavior of target platforms, and thus increasing simulation granularity. However, most existing host-compiled simulators often focus on application behavior only while neglecting effects of hardware/software interactions and associated speed and accuracy tradeoffs in platform modeling. In this dissertation, we focus on host-compiled operating system (OS) and processor modeling techniques, and we introduce novel dynamic timing model management approaches that efficiently improve both accuracy and speed of such models via automatically calibrating the simulation granularity. The contributions of this dissertation are twofold: We first establish an infrastructure for efficient host-compiled multi-core platform simulation by developing (a) abstract models of both real-time OSs and processors that replicate timing-accurate hardware/software interactions and enable full-system co-simulation, and (b) quantitative and analytical studies of host-compiled simulation principles to analyze error bounds and investigate possible improvements. Building on this infrastructure, we further propose specific techniques for improving accuracy and speed tradeoffs in host-compiled simulation by developing (c) an automatic timing granularity adjustment technique based on dynamically observing system state to control the simulation, (d) an out-of-order cache hierarchy modeling approach to efficiently reorder memory access behavior in the presence of temporal decoupling, and (e) a synchronized timing model to align platform threads to run efficiently in parallel simulation. Results as applied to industrial-strength platforms confirm that by providing careful abstractions and dynamic timing management, our models can achieve full-system simulations at equivalent speeds of more than a thousand MIPS with less than 3% timing error. Coupled with the capability to easily adjust simulation parameters and configurations, this demonstrates the benefits of our platform models for early application development and exploration.Electrical and Computer Engineerin

Co-simulation techniques based on virtual platforms for SoC design and verification in power electronics applications

En las últimas décadas, la inversión en el ámbito energético ha aumentado considerablemente. Actualmente, existen numerosas empresas que están desarrollando equipos como convertidores de potencia o máquinas eléctricas con sistemas de control de última generación. La tendencia actual es usar System-on-chips y Field Programmable Gate Arrays para implementar todo el sistema de control. Estos dispositivos facilitan el uso de algoritmos de control más complejos y eficientes, mejorando la eficiencia de los equipos y habilitando la integración de los sistemas renovables en la red eléctrica. Sin embargo, la complejidad de los sistemas de control también ha aumentado considerablemente y con ello la dificultad de su verificación. Los sistemas Hardware-in-the-loop (HIL) se han presentado como una solución para la verificación no destructiva de los equipos energéticos, evitando accidentes y pruebas de alto coste en bancos de ensayo. Los sistemas HIL simulan en tiempo real el comportamiento de la planta de potencia y su interfaz para realizar las pruebas con la placa de control en un entorno seguro. Esta tesis se centra en mejorar el proceso de verificación de los sistemas de control en aplicaciones de electrónica potencia. La contribución general es proporcionar una alternativa a al uso de los HIL para la verificación del hardware/software de la tarjeta de control. La alternativa se basa en la técnica de Software-in-the-loop (SIL) y trata de superar o abordar las limitaciones encontradas hasta la fecha en el SIL. Para mejorar las cualidades de SIL se ha desarrollado una herramienta software denominada COSIL que permite co-simular la implementación e integración final del sistema de control, sea software (CPU), hardware (FPGA) o una mezcla de software y hardware, al mismo tiempo que su interacción con la planta de potencia. Dicha plataforma puede trabajar en múltiples niveles de abstracción e incluye soporte para realizar co-simulación mixtas en distintos lenguajes como C o VHDL. A lo largo de la tesis se hace hincapié en mejorar una de las limitaciones de SIL, su baja velocidad de simulación. Se proponen diferentes soluciones como el uso de emuladores software, distintos niveles de abstracción del software y hardware, o relojes locales en los módulos de la FPGA. En especial se aporta un mecanismo de sincronizaron externa para el emulador software QEMU habilitando su emulación multi-core. Esta aportación habilita el uso de QEMU en plataformas virtuales de co-simulacion como COSIL. Toda la plataforma COSIL, incluido el uso de QEMU, se ha analizado bajo diferentes tipos de aplicaciones y bajo un proyecto industrial real. Su uso ha sido crítico para desarrollar y verificar el software y hardware del sistema de control de un convertidor de 400 kVA

Motion control and synchronisation of multi-axis drive systems

Motion control and synchronisation of multi-axis drive system

The BrainScaleS-2 Neuromorphic Platform — A Report on the Integration and Operation of an Open Science Hardware Platform within EBRAINS

This report presents the challenges encountered and the solutions created for the operation of the BrainScaleS neuromorphic platform, and the overall progress leading to this state at the end of the Human Brain Project (HBP)

Hardware, Software, and Low-Level Control Scheme Development for a Real-Time Autonomous Rover

The objective of this research is to develop a low-cost autonomous rover platform for experiments in autonomous navigation. This thesis describes the design, development, and testing of an autonomous rover platform, based on the commercial, off-the-shelf Tamiya TXT-1 radio controlled vehicle. This vehicle is outfitted with an onboard computer based on the Mini-ITX architecture and an array of sensors for localization and obstacle avoidance, and programmed with Matlab/SimulinkRTM Real-Time Workshop (RTW) utilizing the Linux Real-Time Application Interface (RTAI) operating system.;First, a kinematic model is developed and verified for the rover. Then a proportional-integral-derivative (PID) feedback controller is developed for translational and rotational velocity regulation. Finally, a hybrid navigation controller is developed combining a potential field controller and an obstacle avoidance controller for waypoint tracking.;Experiments are performed to verify the functionality of the kinematic model and the PID velocity controller, and to demonstrate the capabilities of the hybrid navigation controller. These experiments prove that the rover is capable of successfully navigating in an unknown indoor environment. Suggestions for future research include the integration of additional sensors for localization and creation of multiple platforms for autonomous coordination experiments

INTRUSION DETECTION OF A SIMULATED SCADA SYSTEM USING A DATA-DRIVEN MODELING APPROACH

Supervisory Control and Data Acquisition (SCADA) are large, geographically distributed systems that regulate help processes in industries such as nuclear power, transportation or manufacturing. SCADA is a combination of physical, sensing, and communications equipment that is used for monitoring, control and telemetry acquisition actions. Because SCADA often control the distribution of vital resources such as electricity and water, there is a need to protect these cyber-physical systems from those with possible malicious intent. To this end, an Intrusion Detection System (IDS) is utilized to monitor telemetry sources in order to detect unwanted activities and maintain overall system integrity. This dissertation presents the results in developing a behavior-based approach to intrusion detection using a simulated SCADA test bed. Empirical modeling techniques known as Auto Associative Kernel Regression (AAKR) and Auto Associative Multivariate State Estimation Technique (AAMSET) are used to learn the normal behavior of the test bed. The test bed was then subjected to repeated intrusion injection experiments using penetration testing software and exploit codes. Residuals generated from these experiments are then supplied to an anomaly detection algorithm known as the Sequential Probability Ratio Test (SPRT). This approach is considered novel in that the AAKR and AAMSET, combined with the SPRT, have not been utilized previously in industry for cybersecurity purposes. Also presented in this dissertation is a newly developed variable grouping algorithm that is based on the Auto Correlation Function (ACF) for a given set of input data. Variable grouping is needed for these modeling methods to arrive at a suitable set of predictors that return the lowest error in model performance. The developed behavior-based techniques were able to successfully detect many types of intrusions that include network reconnaissance, DoS, unauthorized access, and information theft. These methods would then be useful in detecting unwanted activities of intruders from both inside and outside of the monitored network. These developed methods would also serve to add an additional layer of security. When compared with two separate variable grouping methods, the newly developed grouping method presented in this dissertation was shown to extract similar groups or groups with lower average model prediction errors

Proceedings of the 2011 New York Workshop on Computer, Earth and Space Science

The purpose of the New York Workshop on Computer, Earth and Space Sciences is to bring together the New York area's finest Astronomers, Statisticians, Computer Scientists, Space and Earth Scientists to explore potential synergies between their respective fields. The 2011 edition (CESS2011) was a great success, and we would like to thank all of the presenters and participants for attending. This year was also special as it included authors from the upcoming book titled "Advances in Machine Learning and Data Mining for Astronomy". Over two days, the latest advanced techniques used to analyze the vast amounts of information now available for the understanding of our universe and our planet were presented. These proceedings attempt to provide a small window into what the current state of research is in this vast interdisciplinary field and we'd like to thank the speakers who spent the time to contribute to this volume.Comment: Author lists modified. 82 pages. Workshop Proceedings from CESS 2011 in New York City, Goddard Institute for Space Studie