Safety-related challenges and opportunities for GPUs in the automotive domain

GPUs have been shown to cover the computing performance needs of autonomous driving (AD) systems. However, since the GPUs used for AD build on designs for the mainstream market, they may lack fundamental properties for correct operation under automotive's safety regulations. In this paper, we analyze some of the main challenges in hardware and software design to embrace GPUs as the reference computing solution for AD, with the emphasis in ISO 26262 functional safety requirements.Authors would like to thank Guillem Bernat from Rapita Systems for his technical feedback on this work. The research leading to this work has received funding from the European Re-search Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No. 772773). This work has also been partially supported by the Spanish Ministry of Science and Innovation under grant TIN2015-65316-P and the HiPEAC Network of Excellence. Jaume Abella has been partially supported by the Ministry of Economy and Competitiveness under Ramon y Cajal postdoctoral fellowship number RYC-2013-14717. Carles Hernández is jointly funded by the Spanish Ministry of Economy and Competitiveness and FEDER funds through grant TIN2014-60404-JIN.Peer ReviewedPostprint (author's final draft

Building safe software

Murphy is a set of techniques and tools under investigation for their potential in enhancing the safety of software. This paper describes some of the work which has been done and some which is planned

Safety verification of ADA programs in MURPHY

MURPHY is a experimental methodology, which will include an integrated tool set, for building safety-critical, real-time software. Although it is language independent, many safety-critical software projects are currently planning to use Ada. This paper presents the semantic templates for the verification of the safety of Ada programs using Software Fault Tree Analysis. An example is shown of applying the technique to an Ada program, and the tools in the MURPHY tool set to aid in this type of analysis are described

Completeness, robustness, and safety in real-time software requirements specification

This paper presents an approach to providing a rigorous basis for ascertaining whether or not a given set of software requirements is internally complete, i.e., closed with respect to questions and inferences that can be made on the basis of information included in the specification. Emphasis is placed on aspects of software requirements specifications that previously have not been adequately handled, including timing abstractions, safety, and robustness

A Requirements-based Framework for the Analysis of Socio-technical System Behaviour

Requirements Engineering's theoretical and practical developments typically look forward to the future (i.e. a system to be built). Under certain conditions, however, they can also be used for the analysis of problems related to actual systems in operation. Building on the Jackson/Zave reference model [2] for requirements and specifications, this paper presents a framework useful for the prevention, analysis and communication of designer and operator errors and, importantly, their subtle interactions, so typical in complex socio-technical systems

Hazard Contribution Modes of Machine Learning Components

Amongst the essential steps to be taken towards developing and deploying safe systems with embedded learning-enabled components (LECs) i.e., software components that use ma- chine learning (ML)are to analyze and understand the con- tribution of the constituent LECs to safety, and to assure that those contributions have been appropriately managed. This paper addresses both steps by, first, introducing the notion of hazard contribution modes (HCMs) a categorization of the ways in which the ML elements of LECs can contribute to hazardous system states; and, second, describing how argumentation patterns can capture the reasoning that can be used to assure HCM mitigation. Our framework is generic in the sense that the categories of HCMs developed i) can admit different learning schemes, i.e., supervised, unsupervised, and reinforcement learning, and ii) are not dependent on the type of system in which the LECs are embedded, i.e., both cyber and cyber-physical systems. One of the goals of this work is to serve a starting point for systematizing L analysis towards eventually automating it in a tool

Semantic Support for Log Analysis of Safety-Critical Embedded Systems

Testing is a relevant activity for the development life-cycle of Safety Critical Embedded systems. In particular, much effort is spent for analysis and classification of test logs from SCADA subsystems, especially when failures occur. The human expertise is needful to understand the reasons of failures, for tracing back the errors, as well as to understand which requirements are affected by errors and which ones will be affected by eventual changes in the system design. Semantic techniques and full text search are used to support human experts for the analysis and classification of test logs, in order to speedup and improve the diagnosis phase. Moreover, retrieval of tests and requirements, which can be related to the current failure, is supported in order to allow the discovery of available alternatives and solutions for a better and faster investigation of the problem.Comment: EDCC-2014, BIG4CIP-2014, Embedded systems, testing, semantic discovery, ontology, big dat