Alexander Viehl

Context-sensitive timing simulation of binary embedded software

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.

STELLaR - A Case-Study on SysTEmaticaLLy Embedding a Traffic Light Recognition

In this paper we present an embedded implementation of a Traffic Light Recognition (TLR) on a low-cost FPGA device with low memory usage.We follow a systematic approach where we thoroughly investigate computational hot-spots, and systematically partition the system into hardware and software components which we both optimize. Our implementation is evaluated using an actual FPGA board as Hardware-in-the-Loop (HIL). In contrast to other approaches, we are not restricted to filled lights but also detect other types such as arrows, pedestrians or bicycle ones when provided with training data. With an average performance of 45 fps and minimum 12 fps with ~ 5 Watts of power consumption, our system shows real-time behavior even on high-definition video data with high comparable recognition rates while still obeying automotive constraints such as low power. As far as we know, we are the first ones presenting an embedded TLR solution.

Mission profile aware robustness assessment of automotive power devices

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.

Ontology-Based Design of Space Systems

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.

Trace-based context-sensitive timing simulation considering execution path variations

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.

Impact analysis of AUTOSAR energy saving mechanisms for automotive networks

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.

Context-sensitive timing automata for fast source level simulation

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.

Advanced System-Level Design for Automated Driving

Automated driving (A.D.) requires concurrent execution of multiple complex driving functions on automotive embedded platforms. In general, such systems can be partitioned into early stages including sensor processing, individual perception, and cognition functions and into later, more centralized stages that perform data fusion, planning, and decision making. In this chapter, we exemplarily concentrate on automotive embedded processing systems for perception and cognition problems, however, we expect similar problems also on later stages such as data fusion. For perception and cognition, one can observe a wide gap between required processing power and the achievable embedded realizations which have to fulfill non-functional requirements such as low power and small cost. Furthermore, these systems must perform all processing under strict safety requirements that guarantee deadlines and provide high system robustness.

Compressive sensing-based noise radar for automotive applications

The development of advanced driver assistance systems (ADAS) towards automated driving comes along with an increased density of on-board sensor systems and more traffic participants using those systems. As a result, a highly increased amount of interference sources have to be expected on the future roads, while state-of-the-art frequency modulated continous wave (FMCW) radar systems are prone to interferences. More robust radar systems such as noise radars are so far only applicable to short ranges because of the high effort in signal acquisition. We show, how Compressive Sensing reduces significantly the effort in the signal acquisition enabling the use of the robust noise radar to automotive mid and long ranges. Our promising results demonstrate that feasible range measurements are obtained for a signal to noise ratio of less than 0 dB while using less than 30 % of the reference signals that are needed in standard cross-correlation approaches.

SCDML: A language for Conceptual Data Modeling in Model-based Systems Engineering

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.