Model Checking Real Time Java Using Java PathFinder

The Real Time Specification for Java (RTSJ) is an augmentation of Java for real time applications of various degrees of hardness. The central features of RTSJ are real time threads; user defined schedulers; asynchronous events, handlers, and control transfers; a priority inheritance based default scheduler; non-heap memory areas such as immortal and scoped, and non-heap real time threads whose execution is not impeded by garbage collection. The Robust Software Systems group at NASA Ames Research Center has JAVA PATHFINDER (JPF) under development, a Java model checker. JPF at its core is a state exploring JVM which can examine alternative paths in a Java program (e.g., via backtracking) by trying all nondeterministic choices, including thread scheduling order. This paper describes our implementation of an RTSJ profile (subset) in JPF, including requirements, design decisions, and current implementation status. Two examples are analyzed: jobs on a multiprogramming operating system, and a complex resource contention example involving autonomous vehicles crossing an intersection. The utility of JPF in finding logic and timing errors is illustrated, and the remaining challenges in supporting all of RTSJ are assessed

Proceedings of the 3rd Workshop on Domain-Specific Language Design and Implementation (DSLDI 2015)

The goal of the DSLDI workshop is to bring together researchers and practitioners interested in sharing ideas on how DSLs should be designed, implemented, supported by tools, and applied in realistic application contexts. We are both interested in discovering how already known domains such as graph processing or machine learning can be best supported by DSLs, but also in exploring new domains that could be targeted by DSLs. More generally, we are interested in building a community that can drive forward the development of modern DSLs. These informal post-proceedings contain the submitted talk abstracts to the 3rd DSLDI workshop (DSLDI'15), and a summary of the panel discussion on Language Composition

Safe and Secure Support for Public Safety Networks

International audienceAs explained by Tanzi et al. in the first volume of this book, communicating and autonomous devices will surely have a role to play in the future Public Safety Networks. The “communicating” feature comes from the fact that the information should be delivered in a fast way to rescuers. The “autonomous” characteristic comes from the fact that rescuers should not have to concern themselves about these objects: they should perform their mission autonomously so as not to delay the intervention of the rescuers, but rather to assist them efficiently and reliably.</p

Teaching Concurrent Software Design: A Case Study Using Android

In this article, we explore various parallel and distributed computing topics from a user-centric software engineering perspective. Specifically, in the context of mobile application development, we study the basic building blocks of interactive applications in the form of events, timers, and asynchronous activities, along with related software modeling, architecture, and design topics.Comment: Submitted to CDER NSF/IEEE-TCPP Curriculum Initiative on Parallel and Distributed Computing - Core Topics for Undergraduate

Mapping and Optimizing Communication in ROS 2-based Applications on Configurable System-on-Chip Platforms

The robot operating system is the de-facto standard for designing and implementing robotics applications. Several previous works deal with the integration of heterogeneous accelerators into ROS-based applications. One of these approaches is ReconROS, which enables nodes to be completely mapped to hardware. The follow-up work fpgaDDS extends ReconROS by an intra-FPGA data distribution service to process topic-based communication between nodes entirely in hardware. However, the application of this approach is strictly limited to communication between nodes implemented in hardware only. This paper introduces gateways to close the gap between topic communication in hardware and software. Gateways aim to reduce data transfers between hardware and software by synchronizing a hardware-and software-mapped topic. As a result, data must be transferred only once compared to a separate data transmission for each subscribing hardware node in the baseline. Our measurements show significant speedups in multi-subscriber scenarios with large message sizes. From the conclusions of these measurements, we present a methodology for the communication mapping of ROS 2 computation graphs. In the evaluation, an autonomous driving real-world example benefits from the gateway and achieves a speedup of 1.4

C-MOS array design techniques: SUMC multiprocessor system study

The current capabilities of LSI techniques for speed and reliability, plus the possibilities of assembling large configurations of LSI logic and storage elements, have demanded the study of multiprocessors and multiprocessing techniques, problems, and potentialities. Evaluated are three previous systems studies for a space ultrareliable modular computer multiprocessing system, and a new multiprocessing system is proposed that is flexibly configured with up to four central processors, four 1/0 processors, and 16 main memory units, plus auxiliary memory and peripheral devices. This multiprocessor system features a multilevel interrupt, qualified S/360 compatibility for ground-based generation of programs, virtual memory management of a storage hierarchy through 1/0 processors, and multiport access to multiple and shared memory units

Optimization of deep learning algorithms for an autonomous RC vehicle

Dissertação de mestrado em Engenharia InformáticaThis dissertation aims to evaluate and improve the performance of deep learning (DL) algorithms to autonomously drive a vehicle, using a Remo Car (an RC vehicle) as testbed. The RC vehicle was built with a 1:10 scaled remote controlled car and fitted with an embedded system and a video camera to capture and process real-time image data. Two different embedded systems were comparatively evaluated: an homogeneous system, a Raspberry Pi 4, and an heterogeneous system, a NVidia Jetson Nano. The Raspberry Pi 4 with an advanced 4-core ARM device supports multiprocessing, while the Jetson Nano, also with a 4-core ARM device, has an integrated accelerator, a 128 CUDA-core NVidia GPU. The captured video is processed with convolutional neural networks (CNNs), which interpret image data of the vehicle’s surroundings and predict critical data, such as lane view and steering angle, to provide mechanisms to drive on its own, following a predefined path. To improve the driving performance of the RC vehicle, this work analysed the programmed DL algorithms, namely different computer vision approaches for object detection and image classification, aiming to explore DL techniques and improve their performance at the inference phase. The work also analysed the computational efficiency of the control software, while running intense and complex deep learning tasks in the embedded devices, and fully explored the advanced characteristics and instructions provided by the two embedded systems in the vehicle. Different machine learning (ML) libraries and frameworks were analysed and evaluated: TensorFlow, TensorFlow Lite, Arm NN, PyArmNN and TensorRT. They play a key role to deploy the relevant algorithms and to fully engage the hardware capabilities. The original algorithm was successfully optimized and both embedded systems could perfectly handle this workload. To understand the computational limits of both devices, an additional and heavy DL algorithm was developed that aimed to detect traffic signs. The homogeneous system, the Raspberry Pi 4, could not deliver feasible low-latency values, hence the detection of traffic signs was not possible in real-time. However, a great performance improvement was achieved using the heterogeneous system, Jetson Nano, enabling their CUDA-cores to process the additional workload.Esta dissertação tem como objetivo avaliar e melhorar o desempenho de algoritmos de deep learning (DL) orientados à condução autónoma de veículos, usando um carro controlado remotamente como ambiente de teste. O carro foi construído usando um modelo de um veículo de controlo remoto de escala 1:10, onde foi colocado um sistema embebido e uma câmera de vídeo para capturar e processar imagem em tempo real. Dois sistemas embebidos foram comparativamente avaliados: um sistema homogéneo, um Raspberry Pi 4, e um sistema heterogéneo, uma NVidia Jetson Nano. O Raspberry Pi 4 possui um processador ARM com 4 núcleos, suportando multiprocessamento. A Jetson Nano, também com um processador ARM de 4 núcleos, possui uma unidade adicional de processamento com 128 núcleos do tipo CUDA-core. O vídeo capturado e processado usando redes neuronais convolucionais (CNN), interpretando o meio envolvente do veículo e prevendo dados cruciais, como a visibilidade da linha da estrada e o angulo de direção, de forma a que o veículo consiga conduzir de forma autónoma num determinado ambiente. De forma a melhorar o desempenho da condução autónoma do veículo, diferentes algoritmos de deep learning foram analisados, nomeadamente diferentes abordagens de visão por computador para detecção e classificação de imagens, com o objetivo de explorar técnicas de CNN e melhorar o seu desempenho na fase de inferência. A dissertação também analisou a eficiência computacional do software usado para a execução de tarefas de aprendizagem profunda intensas e complexas nos dispositivos embebidos, e explorou completamente as características avançadas e as instruções fornecidas pelos dois sistemas embebidos no veículo. Diferentes bibliotecas e frameworks de machine learning foram analisadas e avaliadas: TensorFlow, TensorFlow Lite, Arm NN, PyArmNN e TensorRT. Estes desempenham um papel fulcral no provisionamento dos algoritmos de deep learning para tirar máximo partido das capacidades do hardware usado. O algoritmo original foi otimizado com sucesso e ambos os sistemas embebidos conseguiram executar os algoritmos com pouco esforço. Assim, para entender os limites computacionais de ambos os dispositivos, um algoritmo adicional mais complexo de deep learning foi desenvolvido com o objetivo de detectar sinais de transito. O sistema homogéneo, o Raspberry Pi 4, não conseguiu entregar valores viáveis de baixa latência, portanto, a detecção de sinais de trânsito não foi possível em tempo real, usando este sistema. No entanto, foi alcançada uma grande melhoria de desempenho usando o sistema heterogeneo, Jetson Nano, que usaram os seus núcleos CUDA adicionais para processar a carga computacional mais intensa

Comparative Analysis Of Fault-Tolerance Techniques For Space Applications

Fault-tolerance technique enables a system or application to continue working even if some fault /error occurs in a system. Therefore, it is vital to choose appropriate fault tolerant technique best suited to our application. In case of real-time embedded systems in a space project, the importance of such techniques becomes more critical. In space applications, there is minor or no possibility of maintenance and faults occurrence may lead to serious consequences in terms of partial or complete mission failure. This paper describes the comparison of various fault tolerant techniques for space applications. This also suggests the suitability of these techniques in particular scenario.  The study of fault tolerance techniques relevant to real-time embedded systems and on-board space applications (satellites) is given due importance. This study will not only summarize fault tolerant techniques but also describe their strengths. The paper describes the future trends of faults-tolerance techniques in space applications. This effort may help space system engineers and scientists to select suitable fault-tolerance technique for their mission.