Semi-symbolic Simulation and Analysis of Deviation Propagation of Feature Coordination in Cyber-physical Systems

For studying the effects of deviations for uncertain inputs of systems, often multi-run simulation is employed, which is time-consuming. Unfortunately, such simulations also do not directly support the traceability of such effects. A semi-symbolic modeling approach based on Affine Arithmetic Forms allows the representation of uncertainty in terms of ranges. Simulations of such models directly include propagation of deviations and their traceability. This paper presents such a semi-symbolic model of a cyber-physical system including coordination of safety-critical and interacting features. For feature coordination, this model introduces handling discrete uncertainty with two different behavioral modes and their integration. Based on this model, a single simulation run allowed us studying the effects of several deviations. In addition, this modeling approach facilitates specific analyses of deviations based on the traceability information. As a result from simulation and analyses, we got a better understanding of the different deviation propagations within our model

Symbolic Simulation of Mixed-Signal Systems with Extended Affine Arithmetic

Mixed-signal systems combine analog circuits with digital hardware and software systems. A particular challenge is the sensitivity of analog parts to even small deviations in parameters, or inputs. Parameters of circuits and systems such as process, voltage, and temperature are never accurate; we hence model them as uncertain values (‘uncertainties’). Uncertain parameters and inputs can modify the dynamic behavior and lead to properties of the system that are not in specified ranges. For verification of mixed- signal systems, the analysis of the impact of uncertainties on the dynamical behavior plays a central role. Verification of mixed-signal systems is usually done by numerical simulation. A single numerical simulation run allows designers to verify single parameter values out of often ranges of uncertain values. Multi-run simulation techniques such as Monte Carlo Simulation, Corner Case simulation, and enhanced techniques such as Importance Sampling or Design-of-Experiments allow to verify ranges – at the cost of a high number of simulation runs, and with the risk of not finding potential errors. Formal and symbolic approaches are an interesting alternative. Such methods allow a comprehensive verification. However, formal methods do not scale well with heterogeneity and complexity. Also, formal methods do not support existing and established modeling languages. This fact complicates its integration in industrial design flows. In previous work on verification of Mixed-Signal systems, Affine Arithmetic is used for symbolic simulation. This allows combining the high coverage of formal methods with the ease-of use and applicability of simulation. Affine Arithmetic computes the propagation of uncertainties through mostly linear analog circuits and DSP methods in an accurate way. However, Affine Arithmetic is currently only able to compute with contiguous regions, but does not permit the representation of and computation with discrete behavior, e.g. introduced by software. This is a serious limitation: in mixed-signal systems, uncertainties in the analog part are often compensated by embedded software; hence, verification of system properties must consider both analog circuits and embedded software. The objective of this work is to provide an extension to Affine Arithmetic that allows symbolic computation also for digital hardware and software systems, and to demonstrate its applicability and scalability. Compared with related work and state of the art, this thesis provides the following achievements: 1. The thesis introduces extended Affine Arithmetic Forms (XAAF) for the representation of branch and merge operations. 2. The thesis describes arithmetic and relational operations on XAAF, and reduces over-approximation by using an LP solver. 3. The thesis shows and discusses ways to integrate this XAAF into existing modeling languages, in particular SystemC. This way, breaks in the design flow can be avoided. The applicability and scalability of the approach is demonstrated by symbolic simulation of a Delta-Sigma Modulator and a PLL circuit of an IEEE 802.15.4 transceiver system

Symbolic Simulation of Mixed-Signal Systems with Extended Affine Arithmetic

Mixed-signal systems combine analog circuits with digital hardware and software systems. A particular challenge is the sensitivity of analog parts to even small deviations in parameters, or inputs. Parameters of circuits and systems such as process, voltage, and temperature are never accurate; we hence model them as uncertain values (‘uncertainties’). Uncertain parameters and inputs can modify the dynamic behavior and lead to properties of the system that are not in specified ranges. For verification of mixed- signal systems, the analysis of the impact of uncertainties on the dynamical behavior plays a central role. Verification of mixed-signal systems is usually done by numerical simulation. A single numerical simulation run allows designers to verify single parameter values out of often ranges of uncertain values. Multi-run simulation techniques such as Monte Carlo Simulation, Corner Case simulation, and enhanced techniques such as Importance Sampling or Design-of-Experiments allow to verify ranges – at the cost of a high number of simulation runs, and with the risk of not finding potential errors. Formal and symbolic approaches are an interesting alternative. Such methods allow a comprehensive verification. However, formal methods do not scale well with heterogeneity and complexity. Also, formal methods do not support existing and established modeling languages. This fact complicates its integration in industrial design flows. In previous work on verification of Mixed-Signal systems, Affine Arithmetic is used for symbolic simulation. This allows combining the high coverage of formal methods with the ease-of use and applicability of simulation. Affine Arithmetic computes the propagation of uncertainties through mostly linear analog circuits and DSP methods in an accurate way. However, Affine Arithmetic is currently only able to compute with contiguous regions, but does not permit the representation of and computation with discrete behavior, e.g. introduced by software. This is a serious limitation: in mixed-signal systems, uncertainties in the analog part are often compensated by embedded software; hence, verification of system properties must consider both analog circuits and embedded software. The objective of this work is to provide an extension to Affine Arithmetic that allows symbolic computation also for digital hardware and software systems, and to demonstrate its applicability and scalability. Compared with related work and state of the art, this thesis provides the following achievements: 1. The thesis introduces extended Affine Arithmetic Forms (XAAF) for the representation of branch and merge operations. 2. The thesis describes arithmetic and relational operations on XAAF, and reduces over-approximation by using an LP solver. 3. The thesis shows and discusses ways to integrate this XAAF into existing modeling languages, in particular SystemC. This way, breaks in the design flow can be avoided. The applicability and scalability of the approach is demonstrated by symbolic simulation of a Delta-Sigma Modulator and a PLL circuit of an IEEE 802.15.4 transceiver system

Novel metrics for Analog Mixed-Signal coverage

Penelitian ini menggunakan penelitian kualitatif yang berlokasi di KUA Kecamatan Somba Opu. Pendekatan penelitian yang digunakan adalah pendekatan bimbingan penyuluhan Islam dan pendekatan sosiologi. Sumber data dalam penelitian ini adalah sumber data primer dan sumber data sekunder. Tehnik pengumpulan data yang digunakan dalam penelitian ini adalah observasi, wawancara mendalam, dan dokumentasi, dengan teknik analisis data adalah reduksi data, penyajian data dan penarikan kesimpula

Embedded tutorial: analog-/mixed-signal verification methods for AMS coverage analysis

Analog-/Mixed-Signal (AMS) design verification is one of the most challenging and time consuming tasks of todays complex system on chip (SoC) designs. In contrast to digital system design, AMS designers have to deal with a continuous state space of conservative quantities, highly nonlinear relationships, non-functional influences, etc. enlarging the number of possibly critical scenarios to infinity. In this special session we demonstrate the verification of functional properties using simulative and formal methods. We combine different approaches including automated abstraction and refinement of mixed-level models, state-space discretization as well as affine arithmetic. To reach sufficient verification coverage with reasonable time and effort, we use enhanced simulation schemes to avoid conventional simulation drawbacks