34,749 research outputs found
RTL2RTL Formal Equivalence: Boosting the Design Confidence
Increasing design complexity driven by feature and performance requirements
and the Time to Market (TTM) constraints force a faster design and validation
closure. This in turn enforces novel ways of identifying and debugging
behavioral inconsistencies early in the design cycle. Addition of incremental
features and timing fixes may alter the legacy design behavior and would
inadvertently result in undesirable bugs. The most common method of verifying
the correctness of the changed design is to run a dynamic regression test suite
before and after the intended changes and compare the results, a method which
is not exhaustive. Modern Formal Verification (FV) techniques involving new
methods of proving Sequential Hardware Equivalence enabled a new set of
solutions for the given problem, with complete coverage guarantee. Formal
Equivalence can be applied for proving functional integrity after design
changes resulting from a wide variety of reasons, ranging from simple pipeline
optimizations to complex logic redistributions. We present here our experience
of successfully applying the RTL to RTL (RTL2RTL) Formal Verification across a
wide spectrum of problems on a Graphics design. The RTL2RTL FV enabled checking
the design sanity in a very short time, thus enabling faster and safer design
churn. The techniques presented in this paper are applicable to any complex
hardware design.Comment: In Proceedings FSFMA 2014, arXiv:1407.195
New results on pushdown module checking with imperfect information
Model checking of open pushdown systems (OPD) w.r.t. standard branching
temporal logics (pushdown module checking or PMC) has been recently
investigated in the literature, both in the context of environments with
perfect and imperfect information about the system (in the last case, the
environment has only a partial view of the system's control states and stack
content). For standard CTL, PMC with imperfect information is known to be
undecidable. If the stack content is assumed to be visible, then the problem is
decidable and 2EXPTIME-complete (matching the complexity of PMC with perfect
information against CTL). The decidability status of PMC with imperfect
information against CTL restricted to the case where the depth of the stack
content is visible is open. In this paper, we show that with this restriction,
PMC with imperfect information against CTL remains undecidable. On the other
hand, we individuate an interesting subclass of OPDS with visible stack content
depth such that PMC with imperfect information against the existential fragment
of CTL is decidable and in 2EXPTIME. Moreover, we show that the program
complexity of PMC with imperfect information and visible stack content against
CTL is 2EXPTIME-complete (hence, exponentially harder than the program
complexity of PMC with perfect information, which is known to be
EXPTIME-complete).Comment: In Proceedings GandALF 2011, arXiv:1106.081
Cost Preserving Bisimulations for Probabilistic Automata
Probabilistic automata constitute a versatile and elegant model for
concurrent probabilistic systems. They are equipped with a compositional theory
supporting abstraction, enabled by weak probabilistic bisimulation serving as
the reference notion for summarising the effect of abstraction. This paper
considers probabilistic automata augmented with costs. It extends the notions
of weak transitions in probabilistic automata in such a way that the costs
incurred along a weak transition are captured. This gives rise to
cost-preserving and cost-bounding variations of weak probabilistic
bisimilarity, for which we establish compositionality properties with respect
to parallel composition. Furthermore, polynomial-time decision algorithms are
proposed, that can be effectively used to compute reward-bounding abstractions
of Markov decision processes in a compositional manner
Verification of Hierarchical Artifact Systems
Data-driven workflows, of which IBM's Business Artifacts are a prime
exponent, have been successfully deployed in practice, adopted in industrial
standards, and have spawned a rich body of research in academia, focused
primarily on static analysis. The present work represents a significant advance
on the problem of artifact verification, by considering a much richer and more
realistic model than in previous work, incorporating core elements of IBM's
successful Guard-Stage-Milestone model. In particular, the model features task
hierarchy, concurrency, and richer artifact data. It also allows database key
and foreign key dependencies, as well as arithmetic constraints. The results
show decidability of verification and establish its complexity, making use of
novel techniques including a hierarchy of Vector Addition Systems and a variant
of quantifier elimination tailored to our context.Comment: Full version of the accepted PODS pape
The Computational Structure of Spike Trains
Neurons perform computations, and convey the results of those computations
through the statistical structure of their output spike trains. Here we present
a practical method, grounded in the information-theoretic analysis of
prediction, for inferring a minimal representation of that structure and for
characterizing its complexity. Starting from spike trains, our approach finds
their causal state models (CSMs), the minimal hidden Markov models or
stochastic automata capable of generating statistically identical time series.
We then use these CSMs to objectively quantify both the generalizable structure
and the idiosyncratic randomness of the spike train. Specifically, we show that
the expected algorithmic information content (the information needed to
describe the spike train exactly) can be split into three parts describing (1)
the time-invariant structure (complexity) of the minimal spike-generating
process, which describes the spike train statistically; (2) the randomness
(internal entropy rate) of the minimal spike-generating process; and (3) a
residual pure noise term not described by the minimal spike-generating process.
We use CSMs to approximate each of these quantities. The CSMs are inferred
nonparametrically from the data, making only mild regularity assumptions, via
the causal state splitting reconstruction algorithm. The methods presented here
complement more traditional spike train analyses by describing not only spiking
probability and spike train entropy, but also the complexity of a spike train's
structure. We demonstrate our approach using both simulated spike trains and
experimental data recorded in rat barrel cortex during vibrissa stimulation.Comment: Somewhat different format from journal version but same conten
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