We study cutoff results for parameterized verification and synthesis of guarded protocols, as introduced by Emerson and Kahlon (2000). Guarded protocols describe systems of processes whose transitions are enabled or disabled depending on the existence of other processes in certain local states. Cutoff results reduce reasoning about systems with an arbitrary number of processes to systems of a determined, fixed size. Our work is based on the observation that existing cutoff results for guarded protocols are often impractical, since they scale linearly in the number of local states of processes in the system. We provide new cutoffs that scale not with the number of local states, but with the number of guards in the system, which is in many cases much smaller. Furthermore, we consider natural extensions of the classes of systems and specifications under consideration, and present results for problems that have not been known to admit cutoffs before.
A neural network based technique is introduced which hides the control latency of reconfigurable interconnection networks (INs) in shared memory multiprocessors. Such INs require complex control mechanisms to reconfigure the IN on demand, in order to satisfy processor-memory accesses. Hiding the control latency seen by each access improves multiprocessor performance significantly. The new technique hides control latency by employing a time-delay neural network (TDNN) as a prediction technique that learns the current processor-memory access patterns and predicts the need to reconfigure the IN. Training and prediction of the TDNN is performed online. Based on three experiments, the TDNN is able to learn repetitive patterns and predict the need to reconfigure the IN thus, effectively hiding control latency of processor-memory accesses
We consider the following model repair problem: given a finite Kripke structure M and a specification formula η in some modal or temporal logic, determine if M contains a substructure M ′ (with the same initial state) that satisfies η. Thus, M can be "repaired" to *
We report on the last four editions of the reactive synthesis competition (SYNTCOMP 2018(SYNTCOMP -2021. We briefly describe the evaluation scheme and the experimental setup of SYNTCOMP. Then, we introduce new benchmark classes that have been added to the SYNTCOMP library and give an overview of the participants of SYNTCOMP. Finally, we present and analyze the results of our experimental evaluations, including a ranking of tools with respect to quantity and quality of solutions.
We present an algorithm for solving two-player safety games that combines a mixed forward/backward search strategy with a symbolic representation of the state space. By combining forward and backward exploration, our algorithm can synthesize strategies that are eager in the sense that they try to prevent progress towards the error states as soon as possible, whereas standard backwards algorithms often produce permissive solutions that only react when absolutely necessary. We provide experimental results for two new sets of benchmarks, as well as the benchmark set of the Reactive Synthesis Competition (SYNTCOMP) 2017. The results show that our algorithm in many cases produces more eager strategies than a standard backwards algorithm, and solves a number of benchmarks that are intractable for existing tools. Finally, we observe a connection between our algorithm and a recently proposed algorithm for the synthesis of controllers that are robust against disturbances, pointing to possible future applications.
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