Runtime Verification (RV) and Anomaly Detection (AD) are crucial for ensuring the reliability of cyber-physical systems, but existing methods often suffer from high computational costs and lack of explainability. This paper presents a novel approach that integrates formal methods into anomaly detection, transforming complex system models into efficient classification tasks. By combining the strengths of RV and AD, our method significantly improves detection efficiency while providing explainability for failure causes. Our approach offers a promising solution for enhancing the safety and reliability of critical systems.

Deep Neural Networks (DNNs) are increasingly used in safety-critical Cyber-Physical Systems (CPS), requiring rigorous verification. Existing methods struggle with scalability and over-approximation. We introduce Affine Arithmetic Decision Diagrams (AADDs) for DNN reachability analysis, leveraging affine arithmetic to preserve variable correlations and improve precision. Our approach provides a structured symbolic representation of network decisions, enabling efficient and accurate verification. Additionally, AADDs unify verification for DNNs and Analog-Mixed-Signal (AMS) systems, supporting holistic safety analysis.

System security requires a solid foundation in both development and operation. During development, performance trade-offs result in security infrastructures that are more or less effective, but usually imperfect. Hence, during operation, runtime monitoring and anomaly detection continuously check for security issues.In this paper, we show how development and operation can be linked. We demonstrate how information and data from development and operation can be aggregated in a digital twin and/or digital thread which is used as the basis for runtime monitoring and anomaly detection. In particular, we address the trade-off between system security and performance in a concrete smart grid system.

Behavioral models are used to describe the statistical behavior of subsystems that are too large for circuit simulation using SPICE. However, performance estimations obtained using these approaches are typically accurate only for relatively simple circuits, where the performance has a linear dependence on the variation parameters of the circuit. In this work, we introduce statistical performance models that employ piecewise linear regression (PWLR) to model circuit nonlinearity and interblock correlations for use within a hierarchical approach. Our algorithm uses sensitivity-based dimensionality reduction and k-means clustering to ensure that the nonlinear models for the circuit are efficient. The advantage of using PWLR models is illustrated using a current mirror and a charge pump under process, voltage and temperature (PVT) variations.

We describe a SysMLv2-based framework for development-operation integration (DevOps) based on a digital twin. The main feature of the framework is its ability to automatically generate digital twin data models to capture and persist data from deployed, operating instances. These data models are generated from and linked with the SysMLv2 models used during model-based development.

The paper reports on the industrial application of SystemC AMS for semi-symbolic analysis. As a case study we analyze the accuracy of a fuel consumption measurement system. As results, two approaches are presented that make formal and symbolic methods more accessible for industrial practice: (1) integration of symbolic simulation into SystemC AMS, which makes the application similar to the established concrete simulation, and (2) uncertainty tables to further simplify the remaining symbolic modeling of inputs and constraints.

We present a case study on virtual prototyping of automotive applications. We address the co-simulation of HW/SW systems involving firmware, communication protocols, and physical/mechanical systems in the context of model-based and agile development processes. The case study compares the Matlab/Simulink and SystemC based approaches by an e-gas benchmark. We compare the simulation performance, modeling capabilities and applicability in different stages of the development process.

This paper deals with the performance verification of analog/mixed-signal (AMS) systems by a mixed symbolic/numeric (nubolic) simulation. The approach is to piggyback the symbolic simulation via code-instrumentation on the existing, numeric SystemC AMS simulator. This permits the combination of symbolic and numeric simulation ("nubolic simulation"). The particular focus in the paper is the handling of the symbolic discrete-event process activations. This permits the symbolic simulation of digital parts of AMS systems modeled by discrete event processes. The approach is demonstrated by the symbolic simulation of a dual-charge-pump PLL of an IEEE 802.15.4 RF transceiver that includes an asynchronous digital counter as a frequency divider.

The following topics are dealt with: Internet of Things; wireless sensor networks; mobile computing; learning (artificial intelligence); autonomous aerial vehicles; protocols; computer network security; data privacy; data analysis; telecommunication traffic.

IoT systems consist of HW/SW systems (e.g. sensors) that are embedded in a physical world, networked and that interact with complex software platforms. The validation of such systems is a challenge and currently mostly done by prototypes. This paper gives an overview of an approach for the simulation-and emulation-based validation for large and complex IoT systems. The validation is supported by a simulation framework that also permits interaction with online-software, e.g. an IoT platform (emulation). The framework is demonstrated by a comprehensive case study. The example consists of the complete IoT "Smart Energy" use case with focus on data privacy by homomorphic encryption.

Formal methods are a promising alternative to simulation-based verification of mixed-signal systems. However, in practice, such methods fail to scale with heterogeneity and complexity of today's analog/mixed-signal systems. Furthermore, it is unclear how they can be integrated into existing verification flows. This paper shows a path to overcome these obstacles. The idea is to use a hierarchical verification flow, in which components can be verified by formal methods or by multirun simulation. To transport verification results across hierarchies, we represent parameters and properties by affine arithmetic decision diagrams. We study to which extent this approach fulfills the needs of practical application by the verification of a phase-locked loop of an IEEE 802.15.4 transceiver system.

Modeling languages first of all support simulation that is considered as reference by designers. Formal verification and synthesis share the same languages. However, they usually require a separate, dedicated compiler. As modeling languages such as SystemC have reached a high complexity, it is very hard to support reasonably large language subsets, and to guarantee consistency with simulation semantics. This paper shows a way to use the simulator itself to generate a formal model of the complete dynamic behavior. Compared with using yet another compiler, this permits to improve consistency with simulation semantics, and to combine simulation with other use-cases. We concretely focus on symbolic simulation of SystemC AMS ([1], [2], IEEE Std 1666.1-2016), for which we generate AADD and BDD for symbolic simulation.

This paper presents the past, present, and perspectives of the SystemC AMS standard as well as several commercial and academic frameworks gravitating around it that have been used to develop use cases in various cyber physical domains. After an overview of the standard, two frameworks are described: the COSIDE environment that supports the virtual prototyping of embedded HW/SW systems and its interaction with AMS circuits. Second, the SICYPHOS framework that integrates SystemC AMS into the overall system development ecosystem. By an example we give details how SystemC AMS can be coupled with other simulation tools (OpenModelica) while keeping simulation speed and accuracy high. Another example shows how SystemC AMS can be used as foundation technology to create specialized user-defined models of computation (MoC).

Analog circuits lack perfect accuracy; noise, PVT-Variations, non-linearities, crosstalk and many other effects cause unforeseen deviations that we also call "uncertainties". In the paper we classify various causes of uncertainties and describe a simple, generic, mathematical model of uncertain signals and systems that is applicable from circuit level up to system level. We show in particular how to use affine arithmetic forms to document uncertainties of different kind in an uncertainty table, from its causes in circuits up to properties at system-level.

On the contrary to the digital world, no coverage definition exists in the Analog/Mixed-Signal (AMS) context. As digital coverage helps digital designers and verification engineers to evaluate their verification progress, analog designers do not have such metrics. This paper proposes a set of different analog coverage metrics, which improve the confidence in AMS circuit verification. We will demonstrate, that no single overall coverage metric exists. However, as with digital coverage, the proposed analog coverage metrics could substantially help in rating the verification process. Illustrated by a complex AMS circuit example we will explore the limits of analog coverage methodologies as well as the benefits on different levels of abstraction ranging from transistor level up to system level.

Among many challenges in designing reliable integrated circuits and embedded systems, the power integrity (PI-) issue is a particularly critical one. Researches and CAD tools have been carried out in the last decades. However, all of them address the problem from a circuit-level perspective. Moreover, the PIproblem cannot be completely verified until layout, the final stage of the design phase. In this paper we propose a new approach to model power consumption that allows PI-simulation at the system level of abstraction, by extending the state-machine based power estimation method. For this purpose, we model power consumption in power states which also includes a statistical model, and in state-transitions modeled by transfer function. The proposed approach is implemented as part of a simulation framework, which uses SystemC-AMS, and is applied on a battery management IC design.

In this paper we present a concept for assessing the robustness of automotive smart power ICs through lab measurements with respect to application variance and parameter spread. Classical compliance to the product specification, where only minimum and maximum values are defined, is not enough to assess device robustness since complex transients of application components cannot be defined within single specification parameters. That is why application fitness becomes a necessary task to reduce device failures, which may occur in the application. One solution would be to enhance traditional lab verification methods with a concept that considers application and parameter spread. This innovative concept is demonstrated on an electronic throttle control application. It has been emulated in real-time, including power amplification and application-relevant parameters. Monte Carlo experiments were carried out within the application space to evaluate the influence of parameter spread on selected system characteristics. Finally, an appropriate metric was used to quantify the robustness of the micro-electronic device within its application.

The paper describes an approach for semi-symbolic analysis of mixed-signal systems that contain discontinuous functions, e.g. due to modeling comparators. For modeling and semi-symbolic simulation, we use extended Affine Arithmetic. Affine Arithmetic is currently limited to accurate analysis of linear functions and mild non-linear functions, but not yet discontinuities. In this paper we extend the approach to also handle discontinuities. For demonstration, we symbolically analyze a ΣΔ-modulator.

The paper proposes a concept that aims at putting the design of embedded systems at a new, higher level of abstraction. Until now, there is a gap of several weeks of modeling efforts between an initial block diagram sketched on a blackboard and first executable models for e.g. architecture exploration and virtual prototyping. Being able to simulate during or short after initial discussions would be of great benefit. The idea is to provide a library of "smart" models that fill missing details by context knowledge represented in ontologies. In particular, communication between software and peripheral units is replaced by (semantic) references to ontologies. The library is partially implemented based on SystemC (TLM, AMS) and work in progress. For demonstration, a model of an electronic throttle control is modeled at semantic level.

The increased design complexity of analog/mixed signal (AMS) systems puts a high pressure on researchers to find the efficient solutions for the verification of these systems. In contrast to digital designs, the nonlinear nature and higher sensitivity of AMS systems to parameter variations make the verification process more difficult. The focus of this paper is the verification of system robustness with respect to frequency and phase properties. For this purpose, the method, which combines simulation with assertion-based approach, is used. Assertions describing desired system properties use simulation results to verify the system behaviour. In order to provide more formal results using simulation-based methods, deviations of system parameters are modeled as ranges and superimposed to the nominal design model. The range-based representation of parameter values delivers formal model of AMS system for the considered parameter set. Hence, its simulation results in the range-based system response containing the set of simulation traces reachable for all parameter values lying in the specified ranges. The applicability and efficiency of the proposed simulation-based formal method is shown through the verification of PLL phase properties with respect to frequency.

Using wireless communication and energy harvesting in automobiles might have significant advantages considering dependability (no wires and contacts) and weight (no cable tree). In this paper, we give a brief overview of the related technologies, surrounding conditions, and methods for design and optimization. As examples, we focus on methods for harvesting kinetic energy and wireless transmission in a tire pressure metering system (TPMS).

Wireless communication in a car has several advantages, given that demanded safety and real-time requirements are fulfilled. This paper presents a wireless MAC protocol designed for the needs of automotive and industrial applications. The proposed MAC protocol provides special support for network traffic prioritization in order to guarantee worst-case message delays for a set of high-prioritized nodes. The performance is further analyzed with a network simulator and compared with the IEEE 802.15.4 standard CSMA/CA protocol.

We report an approach targeted to aid design exploration, early decisioning in model refinement, optimization and trade offs. The approach consists of SystemC AMS coupling with a descriptive functional simulator. System engineering tools are typically used in design and analysis of system prototypes captured at very high level. Naturally, in high level analysis accuracy and detail of results is compromised in lieu simulation speed and design effort. In the presented approach, the much needed abstraction and simulation speed is retained during simulation of platform architecture while near implementation models (RTL, SPICE) may be also be cosimulated with the architecture.

Accurate and sufficient design of AMS signal paths is always being a challenge for system designers requiring high simulation performance of the analog model which also incorporates circuit level non-idealities. The new SystemC AMS extensions offer high simulation performance as well as capabilities of incorporating circuit level non-ideal effects. In this paper we modelled a low Over Sampling Ratio (OSR), second order Sigma Delta (ΣΔ) Analog to Digital Converter (ADC) which incorporates non-ideal effects like sampling jitter, kBT/CSnoise, switch non-linearities, band-gap noise and operational amplifier non-idealities (such as finite gain, finite bandwidth, gain nonlinearity, slew rate, leakage and saturation effect). The ADC shows a performance bottle neck of 16 bits. State-of-Art signal conditioning techniques use adaptive correction methods inside the analog part or inside the DSP part of the ADC making it more complicated to realize. In our design we have implemented the adaptive filtration within the micro-controller to correct the noise ground as well as large signal non-linear effects to produce an output which is 20-bits clean, proving sufficiency of low order and low OSR of a ΣΔ ADC for 20 bit resolution as well as a simplified adaptive filtration scheme alleviating the need of adaptive blocks within the ADC.

SystemC AMS offers high abstraction and simulation speed through models of computation and language features such as static scheduling, constant time stepping, linear solver and dataflow paradigm. We demonstrate that such rich expressiveness can render non-ideal behavior in system level description. Design exploration and refinement from system level down to cycle accurate or circuit level is also demonstrated. The main contribution is that the fine grain characterization can start at system level design.

Networks consisting of many autonomous sensors are gaining importance in real-life applications. Wireless sensor lifetimes still are limited by finite power sources, leading to the need of low-power system designs. A novel holistic approach for system simulation of ultra-low power wireless sensor networks is discussed. Special focus of this paper is parallel modeling of simulation components, adaptive synchronization, power profiling and framework integration. Detailed network power consumption simulation is achieved by not only simulating the sensor nodes—and their various components—themselves, but also the overall system consisting of multiple interacting elements such as a dynamic environment. The framework includes an instruction set simulation in order to enable accurate node simulation as well as extensive power profiling and tracking beyond node borders. The use of adaptive multi-threading leads to considerable hard-and software co-simulation speedup.1

Finite State Machines (FSMs) are a key element of integrated circuits. Hard-coded FSMs do not allow changes after the ASIC production. While an embedded FPGA IP core provides flexibility, it is a complex circuit, requires difficult synthesis tools, and is expensive. This article presents and evaluates a novel architecture that is specifically optimized for implementing reconfigurable finite state machines: Transition-based Reconfigurable FSM (TR-FSM). The architecture shows a considerable reduction in area, delay, and power consumption compared to FPGA architectures with a (nearly) FPGA-like reconfigurability.

Wireless Sensor Networks are gaining more and more importance in various application fields. Often, energy autonomy on the node level is an essential nonfunctional constraint to be met. Therefore, when simulating such networks, the energy consumption on the node level has to be included into the simulation. To make this time consuming task feasible, an overall simulation speedup on the network level is desirable. In this paper, we propose to use techniques similar to those used in Transaction Level Models of Bus Systems.

Simulation-based verification of electronic control units must face demands related to more functionality and less time to verify it. To ensure a reliable system, one must determine how the omnipresent, internal and external variations affect the target response, and find safe bounds for it. The main challenge is to optimally characterize a high number of sources of variation, with a reduced number of simulation runs. The paper conducts more efficient sensitivity and worst-case studies by applying concepts of Design of Experiments: screening to reduce the dimension of the verification space; sequential experiments for sensitivity analysis; gradient-based search for response bounds. The approach is evaluated on simulations of an airbag driver IC and compared with alternative methods.

SystemC is a discrete event simulator that enables the programmer to model complex designs with varying levels of abstraction. In order to improve precision, it can be coupled to more specialized simulators. This article introduces the concept of loose simulator coupling between an analogue simulator and SystemC. It explains the properties and advantages which include a higher simulation performance as well as a higher degree of flexibility. A design example in which SystemC will be connected to SwitcherCad will demonstrate the benefits of loose coupling.

Provides an abstract of the tutorial presentation and a brief professional biography of the presenter. The complete presentation was not made available for publication as part of the conference proceedings.

Recent additions to the system modelling language SystemC, namely SystemC-AMS and OSCI TLM 2.0, provide more efficient ways to model systems within their respective domains. However, most of today’s embedded systems are usually heterogeneous, requiring some way to connect and simulate models from different domains. In this paper we present a first approach on connecting SystemC-AMS models and TLM 2.0 models using temporal decoupling. We show how certain properties of the involved Models of Computation can be exploited to maintain high simulation performance. Using an example to show the feasibility of our approach, we could also observe a certain tradeoff between simulation performance and accuracy. Further on, we discuss semantical issues and decisions that have to be made, when models are connected. As these decisions are typically application-driven, we propose a converter structure that keeps converters simple but also provides ways to model application-specifc behaviour.

This paper gives an overview of the ldquoAutoSUNrdquo approach. AutoSUN is a common initiative of German automobile manufacturers, suppliers, and microelectronic companies. The aim of the initiative is to create and standardize new methods for the specification and verification of non-functional parameters between the different tiers through the whole supply chain.

This paper describes a verification methodology and verification environment for analog/mixed-signal systems. The methodology structures verification into three steps and systematically leads to test benches. The verification environment consists of SystemC classes that support the offline analysis and regression test of typical properties of automotive systems.

Today's heterogeneous embedded systems combine components from different domains, such as software, analogue hardware and digital hardware. The design and implementation of these systems is still a complex and error-prone task due to the different Models of Computations (MoCs), design languages and tools associated with each of the domains. Though making such systems adaptive is technologically feasible, most of the current design methodologies do not explicitely support adaptive architectures. This paper present the ANDRES project. The main objective of ANDRES is the development of a seamless design flow for adaptive heterogeneous embedded systems (AHES) based on the modelling language SystemC. Using domain-specific modelling extensions and libraries, ANDRES will provide means to efficiently use and exploit adaptivity in embedded system design. The design flow is completed by a methodology tools for automatic hardware and software synthesis for adaptive architectures.

Shrinking microelectronic circuits leads to increasing parameter variations. Verification by Monte Carlo and worst case analysis has a limited reliability or requires a very high number of simulation runs. In this paper we give an overview of the semi-symbolic simulation approach. Compared to Monte Carlo and worst case analysis, the semi-symbolic simulation needs only a single simulation run to compute all possible results for given stimuli and parameter variations. For semi-symbolic simulation we model parameter variations and tolerances by symbols. Then, we compute the transient analysis using affine arithmetic. As results of transient analysis we get the system quantities as affine expressions. The affine expressions describe the impact of the parameter variations to the quantities

In this paper we give an overview of new requirements to the modeling and simulation of analog and mixed-signal systems. We give brief examples from two application domains and reveal limitations of established means for modeling and simulation. New challenges to modeling and simulation arise due to close functional coupling between analog hardware and DSP software, tremendous amount of data to be processed, and an increasing impact of physical effects to system behavior. As a potential solution the article discusses means for combining different models of computation

This paper describes methods and a framework for the refinement and analysis of control and signal processing systems. The design starts with an executable specification and its allowed deviations. Refinement steps introduce models of analog or digital implementations, and augment the "ideal" behavior with different sources of uncertainty, such as noise or production tolerances. The framework verifies and analyzes the influence of these uncertainties on system properties using affine arithmetic.

This paper describes a framework for the refinement of control and signal processing functions. The design starts with an executable specification, and allowed deviations thereof. Refinement steps introduce models of analog or digital implementations, and augment the 'ideal' behavior with different sources of uncertainty. The framework verifies and analyzes the influence of these uncertainties on system properties using affine arithmetic.

This paper gives an overview of a methodology for the design of mixed-signal systems. The methods support the user in defining important system parameters such as sampling frequencies, bit widths, or the type of applicable analog-to-digital converters. This is done by a knowledge-based system, which calculates estimations for the resulting system properties. The methodology starts with an abstract specification of the intended behavior, which is mapped onto a block diagram. The block diagram can then be evaluated by knowledge-based methods and by simulation. The methods have been implemented as a prototypical design tool, which has been used for the design of several case studies.

The systematic top-down design of mixed-signal systems requires an abstract specification of the intended functions. However, hybrid systems are systems whose parts are specified using different time models. Specifications of hybrid systems are not purely functional as they also contain structural information. The structural information is introduced by partitioning the specification into blocks with a homogeneous time model. This often leads to inefficient implementations. In order to overcome this problem, a homogeneous representation for behavior of hybrid systems-KIR-is introduced. This representation makes it possible to represent behavior in all time models in a common way so that the separation in different modeling styles is no longer necessary. Rules for re-writing the KIR-graph are given which permit the description of the same behaviour in another time model.