984 research outputs found
Reachability analysis of linear hybrid systems via block decomposition
Reachability analysis aims at identifying states reachable by a system within
a given time horizon. This task is known to be computationally expensive for
linear hybrid systems. Reachability analysis works by iteratively applying
continuous and discrete post operators to compute states reachable according to
continuous and discrete dynamics, respectively. In this paper, we enhance both
of these operators and make sure that most of the involved computations are
performed in low-dimensional state space. In particular, we improve the
continuous-post operator by performing computations in high-dimensional state
space only for time intervals relevant for the subsequent application of the
discrete-post operator. Furthermore, the new discrete-post operator performs
low-dimensional computations by leveraging the structure of the guard and
assignment of a considered transition. We illustrate the potential of our
approach on a number of challenging benchmarks.Comment: Accepted at EMSOFT 202
Reach Set Approximation through Decomposition with Low-dimensional Sets and High-dimensional Matrices
Approximating the set of reachable states of a dynamical system is an
algorithmic yet mathematically rigorous way to reason about its safety.
Although progress has been made in the development of efficient algorithms for
affine dynamical systems, available algorithms still lack scalability to ensure
their wide adoption in the industrial setting. While modern linear algebra
packages are efficient for matrices with tens of thousands of dimensions,
set-based image computations are limited to a few hundred. We propose to
decompose reach set computations such that set operations are performed in low
dimensions, while matrix operations like exponentiation are carried out in the
full dimension. Our method is applicable both in dense- and discrete-time
settings. For a set of standard benchmarks, it shows a speed-up of up to two
orders of magnitude compared to the respective state-of-the art tools, with
only modest losses in accuracy. For the dense-time case, we show an experiment
with more than 10.000 variables, roughly two orders of magnitude higher than
possible with previous approaches
Decision Procedure for Entailment of Symbolic Heaps with Arrays
This paper gives a decision procedure for the validity of en- tailment of
symbolic heaps in separation logic with Presburger arithmetic and arrays. The
correctness of the decision procedure is proved under the condition that sizes
of arrays in the succedent are not existentially bound. This condition is
independent of the condition proposed by the CADE-2017 paper by Brotherston et
al, namely, one of them does not imply the other. For improving efficiency of
the decision procedure, some techniques are also presented. The main idea of
the decision procedure is a novel translation of an entailment of symbolic
heaps into a formula in Presburger arithmetic, and to combine it with an
external SMT solver. This paper also gives experimental results by an
implementation, which shows that the decision procedure works efficiently
enough to use
Structural methods to improve the symbolic analysis of Petri nets
Symbolic techniques based on BDDs (Binary Decision Diagrams) have emerged as an efficient strategy for the analysis of Petri nets. The existing techniques for the symbolic encoding of each marking use a fixed set of variables per place, leading to encoding schemes with very low density. This drawback has been previously mitigated by using Zero-Suppressed BDDs, that provide a typical reduction of BDD sizes by a factor of two. Structural Petri net theory provides P-invariants that help to derive more efficient encoding schemes for the BDD representations of markings. P-invariants also provide a mechanism to identify conservative upper bounds for the reachable markings. The unreachable markings determined by the upper bound can be used to alleviate both the calculation of the exact reachability set and the scrutiny of properties. Such approach allows to drastically decrease the number of variables for marking encoding and reduce memory and CPU requirements significantly.Peer ReviewedPostprint (author's final draft
Hybrid Verification for Analog and Mixed-signal Circuits
With increasing design complexity and reliability requirements, analog and mixedsignal
(AMS) verification manifests itself as a key bottleneck. While formal methods and
machine learning have been proposed for AMS verification, these two types of techniques
suffer from their own limitations, with the former being specifically limited by scalability
and the latter by inherent errors in learning-based models.
We present a new direction in AMS verification by proposing a hybrid formal/machinelearning-
based verification technique (HFMV) to combine the best of the two worlds.
HFMV builds formalism on the top of a machine learning model to verify AMS circuits
efficiently while meeting a user-specified confidence level. Guided by formal checks,
HFMV intelligently explores the high-dimensional parameter space of a given design by
iteratively improving the machine learning model. As a result, it leads to accurate failure
prediction in the case of a failing circuit or a reliable pass decision in the case of a good
circuit. Our experimental results demonstrate that the proposed HFMV approach is capable
of identifying hard-to-find failures which are completely missed by a huge number
of random simulation samples while significantly cutting down training sample size and
verification cycle time
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