Formal Verification of Probabilistic SystemC Models with Statistical Model Checking

Transaction-level modeling with SystemC has been very successful in describing the behavior of embedded systems by providing high-level executable models, in which many of them have inherent probabilistic behaviors, e.g., random data and unreliable components. It thus is crucial to have both quantitative and qualitative analysis of the probabilities of system properties. Such analysis can be conducted by constructing a formal model of the system under verification and using Probabilistic Model Checking (PMC). However, this method is infeasible for large systems, due to the state space explosion. In this article, we demonstrate the successful use of Statistical Model Checking (SMC) to carry out such analysis directly from large SystemC models and allow designers to express a wide range of useful properties. The first contribution of this work is a framework to verify properties expressed in Bounded Linear Temporal Logic (BLTL) for SystemC models with both timed and probabilistic characteristics. Second, the framework allows users to expose a rich set of user-code primitives as atomic propositions in BLTL. Moreover, users can define their own fine-grained time resolution rather than the boundary of clock cycles in the SystemC simulation. The third contribution is an implementation of a statistical model checker. It contains an automatic monitor generation for producing execution traces of the model-under-verification (MUV), the mechanism for automatically instrumenting the MUV, and the interaction with statistical model checking algorithms.Comment: Journal of Software: Evolution and Process. Wiley, 2017. arXiv admin note: substantial text overlap with arXiv:1507.0818

Dependability Analysis of Control Systems using SystemC and Statistical Model Checking

Stochastic Petri nets are commonly used for modeling distributed systems in order to study their performance and dependability. This paper proposes a realization of stochastic Petri nets in SystemC for modeling large embedded control systems. Then statistical model checking is used to analyze the dependability of the constructed model. Our verification framework allows users to express a wide range of useful properties to be verified which is illustrated through a case study

Accelerating Mixed-Abstraction SystemC Models on Multi-Core CPUs and GPUs

Functional verification is a critical part in the hardware design process cycle, and it contributes for nearly two-thirds of the overall development time. With increasing complexity of hardware designs and shrinking time-to-market constraints, the time and resources spent on functional verification has increased considerably. To mitigate the increasing cost of functional verification, research and academia have been engaged in proposing techniques for improving the simulation of hardware designs, which is a key technique used in the functional verification process. However, the proposed techniques for accelerating the simulation of hardware designs do not leverage the performance benefits offered by multiprocessors/multi-core and heterogeneous processors available today. With the growing ubiquity of powerful heterogeneous computing systems, which integrate multi-processor/multi-core systems with heterogeneous processors such as GPUs, it is important to utilize these computing systems to address the functional verification bottleneck. In this thesis, I propose a technique for accelerating SystemC simulations across multi-core CPUs and GPUs. In particular, I focus on accelerating simulation of SystemC models that are described at both the Register-Transfer Level (RTL) and Transaction Level (TL) abstractions. The main contributions of this thesis are: 1.) a methodology for accelerating the simulation of mixed abstraction SystemC models defined at the RTL and TL abstractions on multi-core CPUs and GPUs and 2.) An open-source static framework for parsing, analyzing, and performing source-to-source translation of identified portions of a SystemC model for execution on multi-core CPUs and GPUs