32,464 research outputs found

    Efficient First-Order Temporal Logic for Infinite-State Systems

    Get PDF
    In this paper we consider the specification and verification of infinite-state systems using temporal logic. In particular, we describe parameterised systems using a new variety of first-order temporal logic that is both powerful enough for this form of specification and tractable enough for practical deductive verification. Importantly, the power of the temporal language allows us to describe (and verify) asynchronous systems, communication delays and more complex properties such as liveness and fairness properties. These aspects appear difficult for many other approaches to infinite-state verification.Comment: 16 pages, 2 figure

    Parallel symbolic state-space exploration is difficult, but what is the alternative?

    Full text link
    State-space exploration is an essential step in many modeling and analysis problems. Its goal is to find the states reachable from the initial state of a discrete-state model described. The state space can used to answer important questions, e.g., "Is there a dead state?" and "Can N become negative?", or as a starting point for sophisticated investigations expressed in temporal logic. Unfortunately, the state space is often so large that ordinary explicit data structures and sequential algorithms cannot cope, prompting the exploration of (1) parallel approaches using multiple processors, from simple workstation networks to shared-memory supercomputers, to satisfy large memory and runtime requirements and (2) symbolic approaches using decision diagrams to encode the large structured sets and relations manipulated during state-space generation. Both approaches have merits and limitations. Parallel explicit state-space generation is challenging, but almost linear speedup can be achieved; however, the analysis is ultimately limited by the memory and processors available. Symbolic methods are a heuristic that can efficiently encode many, but not all, functions over a structured and exponentially large domain; here the pitfalls are subtler: their performance varies widely depending on the class of decision diagram chosen, the state variable order, and obscure algorithmic parameters. As symbolic approaches are often much more efficient than explicit ones for many practical models, we argue for the need to parallelize symbolic state-space generation algorithms, so that we can realize the advantage of both approaches. This is a challenging endeavor, as the most efficient symbolic algorithm, Saturation, is inherently sequential. We conclude by discussing challenges, efforts, and promising directions toward this goal

    Fluent temporal logic for discrete-time event-based models

    Get PDF
    Fluent model checking is an automated technique for verifying that an event-based operational model satisfies some state-based declarative properties. The link between the event-based and state-based formalisms is defined through fluents which are state predicates whose value are determined by the occurrences of initiating and terminating events that make the fluents values become true or false, respectively. The existing fluent temporal logic is convenient for reasoning about untimed event-based models but difficult to use for timed models. The paper extends fluent temporal logic with temporal operators for modelling timed properties of discrete-time event-based models. It presents two approaches that differ on whether the properties model the system state after the occurrence of each event or at a fixed time rate. Model checking of timed properties is made possible by translating them into the existing untimed framework. Copyright 2005 ACM

    Desynchronization: Synthesis of asynchronous circuits from synchronous specifications

    Get PDF
    Asynchronous implementation techniques, which measure logic delays at run time and activate registers accordingly, are inherently more robust than their synchronous counterparts, which estimate worst-case delays at design time, and constrain the clock cycle accordingly. De-synchronization is a new paradigm to automate the design of asynchronous circuits from synchronous specifications, thus permitting widespread adoption of asynchronicity, without requiring special design skills or tools. In this paper, we first of all study different protocols for de-synchronization and formally prove their correctness, using techniques originally developed for distributed deployment of synchronous language specifications. We also provide a taxonomy of existing protocols for asynchronous latch controllers, covering in particular the four-phase handshake protocols devised in the literature for micro-pipelines. We then propose a new controller which exhibits provably maximal concurrency, and analyze the performance of desynchronized circuits with respect to the original synchronous optimized implementation. We finally prove the feasibility and effectiveness of our approach, by showing its application to a set of real designs, including a complete implementation of the DLX microprocessor architectur

    Memory and information processing in neuromorphic systems

    Full text link
    A striking difference between brain-inspired neuromorphic processors and current von Neumann processors architectures is the way in which memory and processing is organized. As Information and Communication Technologies continue to address the need for increased computational power through the increase of cores within a digital processor, neuromorphic engineers and scientists can complement this need by building processor architectures where memory is distributed with the processing. In this paper we present a survey of brain-inspired processor architectures that support models of cortical networks and deep neural networks. These architectures range from serial clocked implementations of multi-neuron systems to massively parallel asynchronous ones and from purely digital systems to mixed analog/digital systems which implement more biological-like models of neurons and synapses together with a suite of adaptation and learning mechanisms analogous to the ones found in biological nervous systems. We describe the advantages of the different approaches being pursued and present the challenges that need to be addressed for building artificial neural processing systems that can display the richness of behaviors seen in biological systems.Comment: Submitted to Proceedings of IEEE, review of recently proposed neuromorphic computing platforms and system
    • …
    corecore