Efficient state reduction methods for PLA-based sequential circuits

Experiences with heuristics for the state reduction of finite-state machines are presented and two new heuristic algorithms described in detail. Results on machines from the literature and from the MCNC benchmark set are shown. The area of the PLA implementation of the combinational component and the design time are used as figures of merit. The comparison of such parameters, when the state reduction step is included in the design process and when it is not, suggests that fast state-reduction heuristics should be implemented within FSM automatic synthesis systems

Canonical finite state machines for distributed systems

There has been much interest in testing from finite state machines (FSMs) as a result of their suitability for modelling or specifying state-based systems. Where there are multiple ports/interfaces a multi-port FSM is used and in testing, a tester is placed at each port. If the testers cannot communicate with one another directly and there is no global clock then we are testing in the distributed test architecture. It is known that the use of the distributed test architecture can affect the power of testing and recent work has characterised this in terms of local s-equivalence: in the distributed test architecture we can distinguish two FSMs, such as an implementation and a specification, if and only if they are not locally s-equivalent. However, there may be many FSMs that are locally s-equivalent to a given FSM and the nature of these FSMs has not been explored. This paper examines the set of FSMs that are locally s-equivalent to a given FSM M. It shows that there is a unique smallest FSM χmin(M) and a unique largest FSM χmax(M) that are locally s-equivalent to M. Here smallest and largest refer to the set of traces defined by an FSM and thus to its semantics. We also show that for a given FSM M the set of FSMs that are locally s-equivalent to M defines a bounded lattice. Finally, we define an FSM that, amongst all FSMs locally s-equivalent to M, has fewest states. We thus give three alternative canonical FSMs that are locally s-equivalent to an FSM M: one that defines the smallest set of traces, one that defines the largest set of traces, and one with fewest states. All three provide valuable information and the first two can be produced in time that is polynomial in terms of the number of states of M. We prove that the problem of finding an s-equivalent FSM with fewest states is NP-hard in general but can be solved in polynomial time for the special case where there are two ports

Synthesis of multiple-input change asynchronous finite state machines

Asynchronous finite state machines (AFSMS) have been limited because multiple-input changes have been disallowed. In this paper, we present an architecture and synthesis system to overcome this limitation. The AFSM marks potentially hazardous state transitions, and prevents output during them. A synthesis tool to create the AFS M incorporates novel algorithms to detect the hazardous states

Automatic Generation of Models of Microarchitectures

Detailed microarchitectural models are necessary to predict, explain, or optimize the performance of software running on modern microprocessors. Building such models often requires a significant manual effort, as the documentation provided by hardware manufacturers is typically not precise enough. The goal of this thesis is to develop techniques for generating microarchitectural models automatically. In the first part, we focus on recent x86 microarchitectures. We implement a tool to accurately evaluate small microbenchmarks using hardware performance counters. We then describe techniques to automatically generate microbenchmarks for measuring the performance of individual instructions and for characterizing cache architectures. We apply our implementations to more than a dozen different microarchitectures. In the second part of the thesis, we study more general techniques to obtain models of hardware components. In particular, we propose the concept of gray-box learning, and we develop a learning algorithm for Mealy machines that exploits prior knowledge about the system to be learned. Finally, we show how this algorithm can be adapted to minimize incompletely specified Mealy machines—a well-known NP-complete problem. Our implementation outperforms existing exact minimization techniques by several orders of magnitude on a number of hard benchmarks; it is even competitive with state-of-the-art heuristic approaches.Zur Vorhersage, Erklärung oder Optimierung der Leistung von Software auf modernen Mikroprozessoren werden detaillierte Modelle der verwendeten Mikroarchitekturen benötigt. Das Erstellen derartiger Modelle ist oft mit einem hohen Aufwand verbunden, da die erforderlichen Informationen von den Prozessorherstellern typischerweise nicht zur Verfügung gestellt werden. Das Ziel der vorliegenden Arbeit ist es, Techniken zu entwickeln, um derartige Modelle automatisch zu erzeugen. Im ersten Teil beschäftigen wir uns mit aktuellen x86-Mikroarchitekturen. Wir entwickeln zuerst ein Tool, das kleine Microbenchmarks mithilfe von Performance Countern auswerten kann. Danach beschreiben wir Techniken, um automatisch Microbenchmarks zu erzeugen, mit denen die Leistung einzelner Instruktionen gemessen sowie die Cache-Architektur charakterisiert werden kann. Im zweiten Teil der Arbeit betrachten wir allgemeinere Techniken, um Hardwaremodelle zu erzeugen. Wir schlagen das Konzept des “Gray-Box Learning” vor, und wir entwickeln einen Lernalgorithmus für Mealy-Maschinen, der bekannte Informationen über das zu lernende System berücksichtigt. Zum Abschluss zeigen wir, wie dieser Algorithmus auf das Problem der Minimierung unvollständig spezifizierter Mealy-Maschinen übertragen werden kann. Hierbei handelt es sich um ein bekanntes NP-vollständiges Problem. Unsere Implementierung ist in mehreren Benchmarks um Größenordnungen schneller als vorherige Ansätze

Quantum Algorithms for Unate and Binate Covering Problems with Application to Finite State Machine Minimization

Covering problems find applications in many areas of computer science and engineering, such that numerous combinatorial problems can be formulated as covering problems. Combinatorial optimization problems are generally NPhard problems that require an extensive search to find the optimal solution. Exploiting the benefits of quantum computing, we present a quantum oracle design for covering problems, taking advantage of Grover’s search algorithm to achieve quadratic speedup. This paper also discusses applications of the quantum counter in unate covering problems and binate covering problems with some important practical applications, such as finding prime implicants of a Boolean function, implication graphs, and minimization of incompletely specified Finite State Machines

Decomposition and encoding of finite state machines for FPGA implementation

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Learning Linear Temporal Properties

We present two novel algorithms for learning formulas in Linear Temporal Logic (LTL) from examples. The first learning algorithm reduces the learning task to a series of satisfiability problems in propositional Boolean logic and produces a smallest LTL formula (in terms of the number of subformulas) that is consistent with the given data. Our second learning algorithm, on the other hand, combines the SAT-based learning algorithm with classical algorithms for learning decision trees. The result is a learning algorithm that scales to real-world scenarios with hundreds of examples, but can no longer guarantee to produce minimal consistent LTL formulas. We compare both learning algorithms and demonstrate their performance on a wide range of synthetic benchmarks. Additionally, we illustrate their usefulness on the task of understanding executions of a leader election protocol

A sufficient condition to polynomially compute a minimum separating DFA

This is the author’s version of a work that was accepted for publication in Information Sciences. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Information Sciences 370–371 (2016) 204–220. DOI 10.1016/j.ins.2016.07.053.The computation of a minimal separating automaton (MSA) for regular languages has been studied from many different points of view, from synthesis of automata or Grammatical Inference to the minimization of incompletely specified machines or Compositional Verification. In the general case, the problem is NP-complete, but this drawback does not prevent the problem from having a real application in the above-mentioned fields. In this paper, we propose a sufficient condition that guarantees that the computation of the MSA can be carried out with polynomial time complexity. © 2016 Elsevier Inc. All rights reserved.Vázquez-De-Parga Andrade, M.; García Gómez, P.; López Rodríguez, D. (2016). A sufficient condition to polynomially compute a minimum separating DFA. Information Sciences. 370-371:204-220. doi:10.1016/j.ins.2016.07.053S204220370-37

On the complexity of minimum inference of regular sets

We prove results concerning the computational tractability of some problems related to determining minimum realizations of finite samples of regular sets by finite automata and regular expressions