Concerns in areas such as privacy, energy consumption, climate gas emissions, and costs, push the trend of migrating neural network inference from being executed on the cloud to embedded edge devices. We present our novel approach DIA-PASON to overcome restrictions brought on by the computing and application requirements that impede their execution on resource-constrained embedded devices. Our approach addresses these challenges by distributing the inference across multiple computing instances, which could be anything ranging from a multi-CPU configuration on the same SoC to geographically distributed devices. In contrast to recent efforts which tend to apply heuristics to reduce the search space of their problem definition to solve it in a timely fashion, our novel problem definition applies the concept of continuous relaxation to the categorical selection of partitioning, layer fusion, and allocation opportunities. This approach overcomes the problem of the poor exploration of the actual search space that arises when removing potential distribution opportunities during problem simplifications. We conduct numerical simulations and ablation experiments on each one of the configuration parameters of our algorithm by distributing several widely used neural networks. Finally, we compare DIAPASON against the commonly used MoDNN baseline and a state-of-the-art approach, CoopAI, achieving a 44 % and 12 % mean speed-up respectively.

Recently, there has been a great deal of research into improving AI methods and their application. The main focus is on tracking progress, enabling transparent comparisons, and fostering a more profound understanding of AI. In that process, different organizations generate and use plenty of assets that need to be tracked, traced and managed. Moreover, it is important to discover assets relevant for the task at hand. This paper presents research aiming to contribute to answering the question of what is required to exchange and manage AI models and related assets effectively without semantic gaps in an industrial context. We introduce a platform for AI model exchange, which facilitates the usage, exchange, and analysis of AI models and datasets. The platform incorporates an ontology that can foster a more profound common understanding of what is required in these tasks and help tackle the issues mentioned above. Finally, we elucidate the utility of the platform through the illustration of a use case in the context of real-time critical systems.

The development of safety-critical software requires a significant additional effort compared to standard software. Safety mechanisms, e.g., for mitigating hardware errors, have to be designed and integrated into the functional code. This results not only in substantial implementation overhead, but also reduces the overall maintainability of the software. In this paper, we present the Universal Safety Format (USF), which enables a model-driven approach that complies with the separation of concerns principle. Software safety mechanisms are specified as patterns via a domain-agnostic transformation language, separated from the functional software. Various domain-specific tools apply these safety patterns to domain-specific artifacts, such as code or software architecture models. This enables the reuse of safety patterns in multiple designs as well as in a single design to artifacts from different domains.

In recent years artificial neural networks (NNs) have been at the center of research on data processing. However, their high computational demand often prohibits deployment on resource-constrained Industrial IoT Systems. Custom hardware accelerators can enable real-time NN processing on small-scale edge devices but are generally hard to develop and integrate. In this paper we present a hardware generation approach to rapidly create, test, and deploy entire SoC platforms with application-specific NN hardware accelerators. The feasibility of the approach is demonstrated by the generation of a condition monitoring system for high-speed valves.

Fast and accurate predictions of a program's execution time are essential during the design space exploration of embedded systems. In this paper, we present a novel approach for efficient context-sensitive timing simulations based on the LLVM IR code representation. Our approach allows evaluating simultaneously multiple hardware platform configurations with only one simulation run. State-of-the-art solutions are improved by speeding up the simulation throughput relying on the fast LLVM IR JIT execution engine. Results show on average over 94% prediction accuracy and a speedup of 200 times compared to interpretive simulations. The simulation performance reaches up to 300 MIPS when one HW configuration is assessed and it grows up to 1 GIPS evaluating four configurations in parallel. Additionally, we show that our approach can be utilized for producing early timing estimations that support the designers in mapping a system to heterogeneous hardware platforms.

The paper presents an efficient approach to the semantic enrichment of measured sensor data in Wireless Sensor Networks (WSNs), by bridging techniques from Model-driven Software Development (MDSD) and Semantic Web Technology (SWT). Our approach reinforces data interoperability, fostering data sharing and reuse, by utilizing SWT. Model-based and type-agnostic configuration reduces the overall effort for WSN setup and maintenance, which are traditionally complex and time-consuming tasks. The presented approach addresses the problem of large-scale WSN management through the application of SWT in WSN configuration and management without requiring expert knowledge. Additionally, we present a generic architecture and an implementation which is also supplemented by hands-on descriptions of an illustrative use case. Our experimental results demonstrate that our model-based approach provides non-intrusive semantic enrichment with sub-millisecond computational overhead, as well as partially automated configuration of WSNs.

Nowadays, product lines are common practice in the embedded systems domain as they allow for substantial reductions in development costs and the time-to-market by a consequent application of design paradigms such as variability and structured reuse management. In that context, accurate and fast timing predictions are essential for an early evaluation of all relevant variants of a product line concerning target platform properties. Context-sensitive simulations provide attractive benefits for timing analysis. Nevertheless, these simulations depend strongly on a single configuration pair of compiler and hardware platform. To cope with this limitation, we present SIMULTime, a new technique for context-sensitive timing simulation based on the software intermediate representation. The assured simulation throughput significantly increases by simulating simultaneously different hardware hardware platforms and compiler configurations. Multiple accurate timing predictions are produced by running the simulator only once. Our novel approach was applied on several applications showing that SIMULTime increases the average simulation throughput by 90% when at least four configurations are analyzed in parallel.

Transforming a clone-and-own (i.e., new product variants are created by copying and modifying existing artifacts) code structure and development process to a Software Product Line Engineering (PLE) approach is a tedious and error-prone task. Holistic tool support for such a process is highly desirable, especially to lower efforts and to speed up the transformation. Unfortunately, such a holistic toolchain for reverse engineering of variability, supporting variant-centric and platform-centric extraction approaches is not available. In this paper, we present a toolchain covering the first steps for moving a clone-and-own product development to a PLE approach. We validate the first prototype of the toolchain on a case study consisting of industrial firmware for smart motor controllers and we show that even this early prototype reduces time and effort for moving to a configurable platform approach in the sense of PLE.

Security by Design becomes a significant aspect for establishing the Internet-of-Things (IoT) paradigm. In this paper, we present an approach to utilize virtual prototypes (VP) at system level to enable security evaluation along the design process. The proposed VP-based penetration testing framework provides an approach for attack surface and attack behavior modeling. By utilizing a modular, reconfigurable system simulation, an attack scenario can be assessed with different system alternatives. As the VP simulates both hardware (HW) and software (SW) of a single IoT-device as well as the interconnections of different devices a comprehensive system analysis can be executed. Our framework is based on a model-driven approach, which underlines the achieved degree of automation and its potential for industrial application. A comprehensive system analysis tool is the enabler to apply penetration testing, for identifying weak points in the system design and implementation, from early stages in the design flow. The overall approach is demonstrated by an automotive use case derived from real-world security flaws.

This paper presents an ontology-supported approach to the management of design parameters in engineering. This approach aims specifically at enabling Change Impact Analysis through Requirements Traceability and acquainted expert knowledge of design parameters. The approach is suitable for both software and hardware designs. The activities and features are mainly obtained by (1) the application of an ontology-based universal system modeling procedure proposal for model integration, (2) the utilization of a knowledge base for capturing expert knowledge and (3) a semantic Mission Profile Aware Design platform. OWL is used to represent information and the underlying data model can improve knowledge transfer among heterogeneous systems which are common in complex engineering projects. At the same time, effort to perform reasoning on such models can be reduced. A demonstration and hands-on description of two illustrative use cases complements the paper.

We present a novel technique for efficient source level timing simulation of embedded software execution on a target platform. In contrast to existing approaches, the proposed technique can accurately approximate time without requiring a dynamic cache model. Thereby the dramatic reduction in simulation performance inherent to dynamic cache modeling is avoided. Consequently, our approach enables an exploitation of the performance potential of source level simulation for complex microarchitectures that include caches. Our approach is based on recent advances in context-sensitive binary level timing simulation. However, a direct application of the binary level approach to source level simulation reduces simulation performance similarly to dynamic cache modeling. To overcome this performance limitation, we contribute a novel pushdown automaton based simulation technique. The proposed context-sensitive timing automata enable an efficient evaluation of complex simulation logic with little overhead. Experimental results show that the proposed technique provides a speed up of an order of magnitude compared to existing context selection techniques and simple source level cache models. Simulation performance is similar to a state of the art accelerated cache simulation. The accelerated simulation is only applicable in specific circumstances, whereas the proposed approach does not suffer this limitation.

Model-based Systems Engineering requires an intuitive semantically strong data model to enable precise data specification and provide the foundation for fruitful data analyses during data evolution. This paper presents an approach to use the Web Ontology Language (OWL) for specifying a Conceptual Data Model (CDM) being transformed into a format understandable by the Eclipse Modeling Framework (EMF) to profit from powerful data handling and knowledge management functions during runtime. Coalescing OWL with EMF brings up the strength of both approaches leading to considerably better data models with less failure potential and reveal notably more analysis potential by using a common data model specification. This approach also enables the direct application of reasoning functionality for automatic inference of several pieces of knowledge and automatic checks as illustrated by examples from aerospace industry.

We present a fast and accurate timing simulation of binary code execution on complex embedded processors. Underlying block timings are extracted from a preceding hardware execution and differentiated by execution context. Thereby, complex factors, such as caches, can be reflected accurately without explicit modeling. Based on timings observed in one hardware execution, timing of numerous other executions for different inputs can be simulated at an average error below 5% for complex applications on an ARM Cortex-A9 processor.

The heating system, the second largest energy consumer after the power train in electric vehicles (EV), has a significant impact on the overall energy management. We propose an optimized control algorithm for minimizing the energy consumption of the heating system for the passenger cabin. The presented adaptive control strategy combining a learning algorithm and dynamic programming optimizes the amount of energy consumption under consideration of the thermal comfort of EV passengers. Experiments shows that a reduction of energy consumption up to 12 % is achieved without sacrificing the preferred comfort.

In this paper we perform an impact analysis of the AUTOSAR energy saving mechanisms partial networking and pretended networking for automotive networks. We developed novel energy management strategies by exploiting these mechanisms. The strategies are integrated in a multi-level power management framework, which consists of three levels. Based on these strategies, we performed experiments on two production-class Electric Vehicles (EVs) measuring the energy consumption of the Electronic Control Units (ECUs). Results show up to 75.4% energy saving impact on the ECUs energy consumption on our test drives.

This paper presents a novel methodology for deriving Conceptual Data Models in the scope of Model-based Systems Engineering. Based on an assessment of currently employed methodologies, substantial limitations of the state of the art are identified. Consequently, a new methodology, overcoming present shortcomings, is elaborated, containing detailed and prescriptive guidelines for deriving conceptual data models used for representing engineering data in a multi-disciplinary design process. For highlighting the applicability and benefits of the approach, the derivation of a semantically strong conceptual data model in the context of Model-based Space Systems Engineering is presented as a case study.

This paper presents the design and usage of a language for Conceptual Data Modeling in Model-based Systems Engineering. Based on an existing analysis of presently employed data modeling languages, a new conceptual data modeling language is defined that brings together characteristic features from software engineering languages, features from languages classically employed for knowledge engineering, as well as entirely newly developed functional aspects. This language has been applied to model a spacecraft as an example, demonstrating its utility for developing complex, multidisciplinary systems in the scope of Model-based Space Systems Engineering.

In model-based systems engineering a model specifying the system’s design is shared across a variety of disciplines and used to ensure the consistency and quality of the overall design. Existing implementations for describing these system models exhibit a number of shortcomings regarding their approach to data management. In this emerging applications paper, we present the application of an ontology for space system design that provides increased semantic soundness of the underlying standardized data specification, enables reasoners to identify problems in the system, and allows the application of operational knowledge collected over past projects to the system to be designed. Based on a qualitative evaluation driven by data derived from an actual satellite design project, a reflection on the applicability of ontologies in the overall model-based systems engineering approach is pursued.

This paper presents an approach on extending virtual prototyping, commonly used for system verification and design space exploration, for safety analysis. Virtual prototyping will enhance safety analysis, overcoming the challenges resulting from the ever-increasing number of safety-related, complex, interconnected electronic systems. The presented integral fault injection framework enables safety analysis in combination with established system verification methods. It consists of a fault behavior specification methodology and the corresponding, reusable injection tool, with focus on seamless applicability in between functional models in early concept phases and low-level structural models in late design phases. Our approach works with third party compilers and simulators while providing a minimal intrusive approach using existing models. Selected use cases at gate, register-transfer and functional level demonstrate the usage of the approach.

In this paper we propose an approach on energy management for optimizing the energy consumption of both electrical and conventional vehicle's board electronic. The pursued idea exploits degrees of freedom resulting from vehicle functions which are not permanently used during operation. The elaborated framework achieves this by a state-based power optimization approach using partial networking. It selectively shuts down Electronic Control Units (ECUs) based on requirement and criticality. Experimental results show up to 29.6% saving of the ECUs energy consumption on several driving cycles, which were created from real measured data provided by an automotive OEM.

We propose a novel torque vectoring concept for battery electric vehicles propelled by wheel-individual electric machines. Besides vehicle dynamic aspects, mainly addressed in other works, we especially focus on energy efficiency improvements. Our approach is based on a comprehensive four-wheel model taking the tires' nonlinear characteristics into account. A yaw torque optimized for vehicle dynamics and energy efficiency is calculated by a controller and allocated to the wheel hubs by a torque distribution block considering the efficiency characteristics of the electric machines. The resulting torque vectoring control system leads to an energy consumption reduction of around 10% for many driving situations, containing both high and low lateral acceleration scenarios.

The engineering of complex systems is more and more supported through computer-based models that rely on a comprehensive specification of their underlying data. This paper reflects on extensive industrial experience with a sophisticated application of conceptual data modeling, addressing requirements as they arise in the context of space systems engineering. For this purpose identified needs for conceptual data modeling in the scope of Model-Based Systems Engineering are formulated. Established and evolving approaches and technologies for building conceptual data models are characterized, analyzed, and discussed regarding their suitability for modeling engineering data. Based on this analysis of the state of the art, recommendations for the future evolution of conceptual data modeling are formulated.

To overcome the verification gap arising from significantly increased external IP integration and reuse during electronic platform design and composition, we present a model-based approach to specify platform variants. The variants specification is processed automatically by formalizing and solving the integrated constraint sets to derive valid platforms. These constraint sets enable a precise specification of the required platform variants for verification, exploration and test. Experimental results demonstrate the applicability, versatility and scalability of our novel model-based approach.

We present an approach to accurately simulate the temporal behavior of binary embedded software based on timing data generated using static analysis. As the timing of an instruction sequence is significantly influenced by the microarchitecture state prior to its execution, which highly depends on the preceding control flow, a sequence must be separately considered for different control flow paths instead of estimating the influence of basic blocks or single instructions in isolation. We handle the thereby arising issue of an excessive or even infinite number of different paths by considering different execution contexts instead of control flow paths. Related approaches using context-sensitive cycle counts during simulation are limited to simulating the control flow that could be considered during analysis. We eliminate this limitation by selecting contexts dynamically, picking a suitable one when no predetermined choice is available, thereby enabling a context-sensitive simulation of unmodified binary code of concurrent programs, including asynchronous events such as interrupts. In contrast to other approximate binary simulation techniques, estimates are conservative, yet tight, making our approach reliable when evaluating performance goals. For a multi-threaded application the simulation deviates only by 0.24% from hardware measurements while the average overhead is only 50% compared to a purely functional simulation.

Intelligent automotive electronics significantly improved driving safety in the last decades. With the increasing complexity of automotive systems, dependability of the electronic components themselves and of their interaction must be assured to avoid any risk to driving safety due to unexpected failures caused by internal or external faults.

In this paper we propose to exploit so called Mission Profiles to address increasing requirements on safety and power efficiency for automotive power ICs. These Mission Profiles constrain the required device performance space to valid application scenarios. Mission Profile data can be represented in arbitrary forms like temperature histograms or cumulated drive cycle data. Hence, the derivation of realistic verification scenarios on device level requires the generation of environmental properties as e.g. temperatures, board net conditions or currents. For the assessment of real application robustness we present a methodology to extract finite state machines out of measured vehicle data and integrate them in Mission Profiles. Subsequently Markov processes are derived from these finite state machines in order to automatically generate Mission Profile compliant test scenarios for the design and verification process. As a motivating example we show industry fault cases in which missing application fitness to power transient variations finally results in device failure. Verification results based on lab data are outlined and show the benefits of a fully mission profile driven IC verification flow.

This paper presents a real-time capable battery aging estimation to enhance state-of-the-art eco-driving concept with battery maintenance strategy for potentially advancing electric vehicles (EV) sustainability. This methodology focuses not only on estimating battery aging according to the input parameters during driving and resting period but also optimizing withdrawn battery power. This is derived from applied control strategy for state of charge (SoC) and state of health (SoH) modeling. A systematic lithium-based battery model with temperature significance is established as the base of this methodology which is validated under actual operating conditions. The result indicates that eco-driving strategy is advanced by SoH in parallel with SoC estimation, also the optimization of withdrawn battery power might be further adapted to establish advanced driving range prediction. Particularly, the real-time implementation of this methodology might increase the awareness of EV drivers for preventing any accelerated battery aging.

We propose a novel operation strategy for electric vehicles with axle-individual electric machines to improve their energy efficiency in typical driving situations. The developed algorithm is allocating a total torque requested by a velocity controlling system or the driver to the electric machines such that the energy loss is reduced compared to an equal distribution. By taking near-future forecasts into account, the predictive nature of the algorithm leads to a minimized number of clutching processes compared to previous work and thereby contributes to increased comfort and minimized component wear. Overall, an average reduction of up to 25% in the electric machine losses can be achieved for the ARTEMIS driving cycles. At the same time, a reduction of the clutching operations by 70% is possible due to the forecast, compared to algorithms only considering the momentary state.

The HVAC system is considered as the largest auxiliary power load in electric vehicles (EV). Therefore, this paper presents a detailed modeling of an EV-based HVAC system to support a priori prediction of HVAC system energy consumption under consideration of the EV users thermal comfort. This prediction is integrated into a navigation system to allow the driver entering the preferred parameters of thermal comfort and advising the driver about the predicted overall energy consumption. The advice acceptance might increase the awareness of the driver regarding the potential saved energy and leads to an energy-efficient vehicle operation by extending the overall driving range.

To support the reliability assessment of safety-relevant distributed automotive systems and reduce its complexity, this paper presents a novel approach that extends virtual prototyping towards error effect simulation. Besides the common functional and timed system simulation, error injection is used to stress error tolerance mechanisms. A quantitative assessment of the overall system reliability is performed by observing the system reactions and identifying incorrect system behavior. To foster the industrial application, the analysis is integrated in a model-based design flow, starting at the modeling level to assemble and parameterize the virtual prototype and to configure the analysis. The feasibility of the proposed approach is demonstrated by analyzing a representative safety-relevant automotive use case.

In this paper we present a novel approach for mapping interconnected software components onto cores of homogenous MPSoC architectures. The analytic mapping process considers shared memory communication as well as the routing algorithm controlling packet-based communication. The software components are mapped with the constraints of avoiding communication conflicts as well as access conflicts to shared memory resources. The core of the elaborated approach consists of an algorithm for software mapping which is inspired by force-directed scheduling from high-level synthesis. Experimental results show that the presented approach increases the overall system performance by 22% while reducing the average communication latency by 35%. For presenting the major advantages of the developed solution, we optimized an advanced driver assistance system on the Tilera TILEPro64 processor.

This document proposes a new strategy for decelerating a battery electric vehicle from an initial velocity to a final velocity with optimized recuperation of kinetic excess energy. Thereby, we demonstrate a possibility to increase the efficiency - and hence range - without altering the powertrain. The algorithm is implemented in an advanced driver assistance system to guide the driver and - given he accepts the hints - leads to an energy-optimized deceleration trajectory. A situation-adaptive online estimation of influencing parameters, which are not measurable during the drive, is presented. The knowledge of these parameters allows an accurate operation of the proposed functionality. Simulations show an increased energy regeneration of up to 34% for an exemplary vehicle and typical road segments.

In this paper, we present a novel approach for automated mapping of software processes onto the cores of MPSoC architectures using a regular packet-based communication infrastructure. During the mapping determination, the communication distance as well as the routing algorithm for packet-based communication are taken into account. The basic idea of the developed approach is the reduction of communication conflicts on the communication network links for reducing the overall communication latency and hence for increasing the total system performance. The presented approach is based on the idea of force-directed scheduling (FDS) from high-level synthesis and uses forces for determining an optimized process mapping. The approach constrains the cores, that are used for the calculation of possible mappings, by the communication distance of the communicating processes. We present results obtained from an advanced driver assistance system on a Tilera TILEPro64 processor.

In this paper a comprehensive assertion-based verification methodology for the digital, analog and software domain of heterogeneous systems is presented. The proposed methodology combines a novel mixed-signal assertion language and the corresponding automatic verification algorithm. The algorithm translates the heterogeneous temporal properties into observer automata for a semi-formal verification. This enables automatic verification of complex heterogeneous properties that can not be verified by existing approaches. The experimental results show the integration of mixed-signal assertions into a simulation environment and demonstrate the broad applicability and the high value of the evolved solution.

In this paper, a novel approach for integrating static non-preemptive software scheduling in formal bottom-up performance evaluation of embedded system models is described. The presented analysis methodology uses a functional SystemC implementation of communicating processes as input. Necessary model extensions towards capturing of static non-preemptive scheduling are introduced and the integration of the software scheduling in the formal analysis process is explained. The applicability of the approach in an automated design flow is presented using a SystemC model of a JPEG encoder.

This paper presents an approach for cycle-accurate simulation of embedded software by integration in an abstract SystemC model. Compared to existing simulation-based approaches, we present a hybrid method that resolves performance issues by combining the advantages of simulation-based and analytical approaches. In a first step, cycle-accurate static execution time analysis is applied at each basic block of a cross-compiled binary program using static pro-cessor models. After that, the determined timing information is back-annotated into SystemC for fast simulation of all effects that can not be resolved statically. This allows the consideration of data dependencies during run-time and the incorporation of branch pre-diction and cache models by efficient source code instrumentation. The major benefit of our approach is that the generated code can be executed very efficiently on the simulation host with approxi-mately 90 % of the speed of the untimed software without any code instrumentation.

In this paper, a novel design and analysis methodology for simulation-based determination of non-functional properties of a system design, like performance, power consumption and temperature is proposed. For simulation acceleration and handling of complexity issues, the design flow includes automated abstraction of component functionality. Specified platform attributes as dynamic power management and formally declared temporal input stimuli are automatically transformed to non-functional SystemC models. The framework implements the ability for automated online and offline analysis of non-functional system-on-chip properties.

In this paper, we present an approach for control-flow aware communication and conflict analysis of systems of parallel communicating processes. This approach allows to determine the global timing behavior of such a system and to detect communication that might produce conflicts on shared communication resources. Furthermore, we show the incorporation of temporal environment models in order to analyze their influence on the system behavior. Based on the determined conflicts, an automated allocation and binding approach for shared resources to resolve potential access conflicts is proposed. All analysis steps can be performed starting with a TLM SystemC model of the entire system without any need for user interaction. Finally, a SystemC model of a Viterbi decoder is used as case study to demonstrate the capability of our approach.

UML2 and SysML try to adopt techniques known from software development to systems engineering. However, the focus has been put on modeling aspects until now and quantitative performance analysis is not adequately taken into account in early design stages of the system. In this paper, we present our approach for formal and simulation based performance analysis of systems specified with UML2/SysML. The basis of our analysis approach is the detection of communication that synchronize the control flow of the corresponding instances of the system and make the relationship explicit. Using this knowledge, we are able to determine a global timing behavior and violations of this effected by preset constraints. Hence, it is also possible to detect potential conflicts on shared communication resources if a specification of the target architecture is given. With these information it is possible to evaluate system models at an early design stage