LEGaTO: first steps towards energy-efficient toolset for heterogeneous computing

LEGaTO is a three-year EU H2020 project which started in December 2017. The LEGaTO project will leverage task-based programming models to provide a software ecosystem for Made-in-Europe heterogeneous hardware composed of CPUs, GPUs, FPGAs and dataflow engines. The aim is to attain one order of magnitude energy savings from the edge to the converged cloud/HPC.Peer ReviewedPostprint (author's final draft

Using Relocatable Bitstreams for Fault Tolerance

This research develops a method for relocating reconfigurable modules on the Virtex-II (Pro) family of Field Programmable Gate Arrays (FPGAs). A bitstream translation program is developed which correctly changes the location of a partial bitstream that implements a module on the FPGA. To take advantage of relocatable modules, three fault-tolerance circuit designs are developed and tested. This circuit can operate through a fault by efficiently removing the faulty module and replacing it with a relocated module without faults. The FPGA can recover from faults at a known location, without the need for external intervention using an embedded fault recovery system. The recovery system uses an internal PowerPC to relocate the modules and reprogram the FPGA. Due to the limited architecture of the target FPGA and Xilinx tool errors, an FPGA with automatic fault recovery could not be demonstrated. However, the various components needed to do this type of recovery have been implemented and demonstrated individually

Online Model-Based Testing under Uncertainty

Modern software systems are required to operate in a highly uncertain and changing environment. They have to control the satisfaction of their requirements at run-time, and possibly adapt and cope with situations that have not been completely addressed at design-time. Software engineering methods and techniques are, more than ever, forced to deal with change and uncertainty (lack of knowledge) explicitly. For tackling the challenge posed by uncertainty in delivering more reliable systems, this paper proposes a novel online Model-based Testing technique that complements classic test case generation based on pseudo-random sampling strategies with an uncertainty-aware sampling strategy. To deal with system uncertainty during testing, the proposed strategy builds on an Inverse Uncertainty Quantification approach that is related to the discrepancy between the measured data at run-time (while the system executes) and a Markov Decision Process model describing the behavior of the system under test. To this purpose, a conformance game approach is adopted in which tests feed a Bayesian inference calibrator that continuously learns from test data to tune the system model and the system itself. A comparative evaluation between the proposed uncertainty-aware sampling policy and classical pseudo-random sampling policies is also presented using the Tele Assistance System running example, showing the differences in achieved accuracy and efficiency

Data Transport System

To facilitate the WU Smart Home research [21] we built a system that collects data from sensors and uploads the data to the cloud. The system supports data collection from multiple locations (typically apartments) that are independent from each other, endowing the system with two benefit: distributed data collection and alleviating privacy concerns. Each location is managed by a local micro-server (μServer) that is responsible for receiving data packets from sensors and managing their transient storage. Periodically the μServer triggers a data transport process that moves the data to a cloud server where it is stored in a centralized database. In the cloud, the receiving subsystem verifies that the data is coherent and if so, inserts it to the database for permanent storage. Errors are detected and stored in a designated table for review and correction when possible. The database is relational and referential integrity is defined over the schema to force data coherency. The system was designed to support dynamic module loading allowing system deployers to author their own sensor specific module to validate the data in any way they want. iii Important features of the system are configured without code changes. For example, users can define sensor identification rules by editing a configuration file. There is full support for logging. Input-Output and other expressions that can fail are enclosed within a try-except clauses and an overarching error handling module controls critical events. The system is written in Python 2.7.5 [12] and its runs on Linux (on both the μServers and the cloud server) and the embedded code is written in nesC [18] and runs on TinyOS 2.1.2 [15]. The source code is well documented; each module has introduction section, descriptive variable names and CONSTANTS are used, when appropriate inline (expression level) comments are provided. README.txt files are provided for each module. Excluding Python, the system uses the latest versions of every tool or OS that it rely on

Abordagem de Anotações para o Suporte da Gestão Energética de Software em Modelos AMALTHEA

The automotive industry is continuously introducing innovative software features to provide more efficient, safe, and comfortable solutions. Despite the several benefits to the consumer, the evolution of automotive software is also reflected in several challenges, presenting a growing complexity that hinders its development and integration. The adoption of standards and appropriate development methods becomes essential to meet the requirements of the industry. Furthermore, the expansion of automotive software systems is also driving a considerable growth in the number of electronic components installed in a vehicle, which has a significant impact on the electric energy consumption. Thus, the focus on non-functional energy requirements has become increasingly important. This work presents a study focused on the evolution of automotive software considering the development standards, methodologies, as well as approaches for energy requirements management. We propose an automatic and self-contained approach for the support of energy properties management, adopting the model-based open-source framework AMALTHEA. From the analysis of execution or simulation traces, the energy consumption estimation is provided at a fine-grained level and annotated in AMALTHEA models. Thus, we enable the energy analysis and management of the system throughout the entire lifecycle. Additionally, this solution is in line with the AUTOSAR Adaptive standard, allowing the development of energy management strategies for automatic, dynamic, and adaptive systems.A indústria automotiva encontra-se constantemente a introduzir funcionalidades inovadoras através de software, para oferecer soluções mais eficientes, seguras e confortáveis. Apesar dos diversos benefícios para o consumidor, a evolução do software automóvel também se reflete em diversos desafios, apresentando uma crescente complexidade que dificulta o seu desenvolvimento e integração. Desta forma, a adoção de normas e metodologias adequadas para o seu desenvolvimento torna-se essencial para cumprir os requisitos do setor. Adicionalmente, esta expansão das funcionalidades suportadas por software é fonte de um aumento considerável do número de componentes eletrónicos instalados em automóveis. Consequentemente, existe um impacto significativo no consumo de energia elétrica dos sistemas automóveis, sendo cada vez mais relevante o foco nos requisitos não-funcionais deste domínio. Este trabalho apresenta um estudo focado na evolução do software automotivo tendo em conta os padrões e metodologias de desenvolvimento desta área, bem como abordagens para a gestão de requisitos de energia. Através da adoção da ferramenta AMALTHEA, uma plataforma open-source de desenvolvimento baseado em modelos, é proposta uma abordagem automática e independente para a análise de propriedades energéticas. A partir da análise de traços de execução ou de simulação, é produzida uma estimativa pormenorizada do consumo de energia, sendo esta anotada em modelos AMALTHEA. Desta forma, torna-se possível a análise e gestão energética ao longo de todo o ciclo de vida do sistema. Salienta-se que a solução se encontra alinhada com a norma AUTOSAR Adaptive, permitindo o desenvolvimento de estratégias para a gestão energética de sistemas automáticos, dinâmicos e adaptativos

Using the Go Programming Language in Practice

When developing software today, we still use old tools and ideas. Maybe it is time to start from scratch and try tools and languages that are more in line with how we actually want to develop software. The Go Programming Language was created at Google by a rather famous trio: Rob Pike, Ken Thompson and Robert Griesemer. Before introducing Go, the company suffered from their development process not scaling well due to slow builds, uncontrolled dependencies, hard to read code, poor documentation and so on. Go is set out to provide a solution for these issues. The purpose of this master's thesis was to review the current state of the language. This is not only a study of the language itself but an investigation of the whole software development process using Go. The study was carried out from an embedded development perspective which includes an investigation of compilers and cross-compilation. We found that Go is exciting, fun to use and fulfills what is promised in many cases. However, we think the tools need some more time to mature