Wolfgang Rosenstiel

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.

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.

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.

Verification of AUTOSAR Software by SystemC-Based Virtual Prototyping

This chapter focuses on simulation-based verification of AUTOSAR software. It introduces the methodology and technical aspects behind AUTOSAR and outlines the affinities of the concepts of AUTOSAR and SystemC. It discusses in detail how SystemC supports the implementation of AUTOSAR and how SystemC can be applied with respect to the different AUTOSAR layers. Furthermore, it illustrates the different views of car makers and tier 1 suppliers onto the system and discuss how SystemC can support them. Therefore, the different layers of abstraction within TLM (Transaction-Level Modeling) space are introduced and different ways of integrating timing behavior into the entire system are shown. The article is attended by a case study of a traffic sign recognition system, which demonstrates the functional and timing evaluation of the entire system.

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.

An application case study of state-based power optimization with partial networking

In this paper we perform an application case study of state-based power optimization strategies, which exploit the AUTOSAR energy saving mechanism partial networking. We performed energy consumption measurements of the Electronic Control Units (ECUs) on a production-class Electric Vehicle (EV). Based on the measurement results, we analyzed both the applicability and energy saving effect of the strategies. Results show up to 15.0% energy saving impact on the ECUs energy consumption on our test drives.

Safety consideration in state-based power optimization with partial networking - A case study

In this paper we perform a safety analysis while applying state-based power optimization strategies using the AUTOSAR energy saving mechanism partial networking. Based on a case study of a braking system of a production-class Electric Vehicle (EV), we first show which safety issues could be introduced by applying partial networking. Then, based on our energy measurements of the ECU and the pressure measurements of the vacuum container, we show how to apply partial networking by considering all identified safety issues.

Constraint-based platform variants specification for early system verification

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.