An Unsupervised Approach for Automotive Driver Identification

The adoption of on-vehicle monitoring devices allows different entities to gather valuable data about driving styles, which can be further used to infer a variety of information for different purposes, such as fraud detection and driver profiling. In this paper, we focus on the identification of the number of people usually driving the same vehicle, proposing a data analytic work-flow specifically designed to address this problem. Our approach is based on unsupervised learning algorithms working on non-invasive data gathered from a specialized embedded device. In addition, we present a preliminary evaluation of our approach, showing promising driver identification capabilities and a limited computational effort

CONTREX: Design of embedded mixed-criticality CONTRol systems under consideration of EXtra-functional properties

The increasing processing power of today’s HW/SW platforms leads to the integration of more and more functions in a single device. Additional design challenges arise when these functions share computing resources and belong to different criticality levels. The paper presents the CONTREX European project and its preliminary results. CONTREX complements current activities in the area of predictable computing platforms and segregation mechanisms with techniques to consider the extra-functional properties, i.e., timing constraints, power, and temperature. CONTREX enables energy efficient and cost aware design through analysis and optimization of these properties with regard to application demands at different criticality levels

CONTREX: Design of embedded mixed-criticality CONTRol systems under consideration of EXtra-functional properties

The increasing processing power of today’s HW/SW platforms leads to the integration of more and more functions in a single device. Additional design challenges arise when these functions share computing resources and belong to different criticality levels. CONTREX complements current activities in the area of predictable computing platforms and segregation mechanisms with techniques to consider the extra-functional properties, i.e., timing constraints, power, and temperature. CONTREX enables energy efficient and cost aware design through analysis and optimization of these properties with regard to application demands at different criticality levels. This article presents an overview of the CONTREX European project, its main innovative technology (extension of a model based design approach, functional and extra-functional analysis with executable models and run-time management) and the final results of three industrial use-cases from different domain (avionics, automotive and telecommunication).The work leading to these results has received funding from the European Community’s Seventh Framework Programme FP7/2007-2011 under grant agreement no. 611146

Exploring manycore architectures for next-generation HPC systems through the MANGO approach

[EN] The Horizon 2020 MANGO project aims at exploring deeply heterogeneous accelerators for use in High-Performance Computing systems running multiple applications with different Quality of Service (QoS) levels. The main goal of the project is to exploit customization to adapt computing resources to reach the desired QoS. For this purpose, it explores different but interrelated mechanisms across the architecture and system software. In particular, in this paper we focus on the runtime resource management, the thermal management, and support provided for parallel programming, as well as introducing three applications on which the project foreground will be validated.This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 671668.Flich Cardo, J.; Agosta, G.; Ampletzer, P.; Atienza-Alonso, D.; Brandolese, C.; Cappe, E.; Cilardo, A.... (2018). Exploring manycore architectures for next-generation HPC systems through the MANGO approach. Microprocessors and Microsystems. 61:154-170. https://doi.org/10.1016/j.micpro.2018.05.011S1541706

TEXTAROSSA: Towards EXtreme scale Technologies and Accelerators for euROhpc hw/Sw Supercomputing Applications for exascale

International audienceTo achieve high performance and high energy efficiency on near-future exascale computing systems, three key technology gaps needs to be bridged. These gaps include: energy efficiency and thermal control; extreme computation efficiency via HW acceleration and new arithmetics; methods andtools for seamless integration of reconfigurable accelerators in heterogeneous HPC multi-node platforms. TEXTAROSSA aims at tackling this gap through a co-design approach to heterogeneous HPC solutions, supported by the integration and extension of HW and SW IPs, programming models and tools derived from European research

A Framework for Compile-time and Run-time Management of Non-functional Aspects in WSNs Nodes

The quality of realistic complex wireless sensor networks requires several non-functional aspects to be accounted for starting from the early phases of the application development. The most relevant aspects that need to be considered for optimization trade-offs are for sure computational efficiency (in a wide sense) and network lifetime. At lower level these aspects are measured as power/energy consumption and execution time. These are though not the only non-functional aspects to be considered: code size, memory requirement, security, reliability and other properties play often an important role. Accounting for and managing all these aspects explicitly and in an ad-hoc manner for each and every application deployed on a WSN is a time consuming and complex task. This paper proposes a portable, flexible and extendable framework for the description and management of non-functional aspects in the wireless sensor network context