This paper extends the l-complete approximation method developed for time invariant systems to a larger system class, ensuring that the resulting approximation can be realized by a finite state machine. To derive the new abstraction method, called asynchronous l-complete approximation, an asynchronous version of the well-known concepts of state property, memory span and l-completeness is introd...

Bertsekas, D.P. and D.A. Castailon, Parallel synchronous and asynchronous implementations of the auction algorithm, Parallel Computing 17 (1991) 707-732. In this paper we discuss the parallel implementation of the auction algorithm for the classical assignment problenywe show that the algorithm admits a totally asynchronous implementation and we consider several implementations on a shared memo...

Parallel and distributed algorithms have become a necessity in modern machine learning tasks. In this work, we focus on parallel asynchronous gradient descent [1, 2, 3] and propose a zealous variant that minimizes the idle time of processors to achieve a substantial speedup. We then experimentally study this algorithm in the context of training a restricted Boltzmann machine on a large collabor...

We present the first self-stabilizing consensus and replicated state machine for asynchronous message passing systems. The scheme does not require that all participants make a certain number of steps prior to reaching a practically infinite execution where the replicated state machine exhibits the desired behavior. In other words, the system reaches a configuration from which it operates accord...

This work addresses the analysis and validation of CHP specifications for asynchronous circuits, using property verification tools. CHP semantics, initially given in terms of Petri Nets, are reformulated as labeled transition systems. Circuit specifications are translated into an intermediate format (IF) based on communicating extended finite state machines. They are then validated using the IF...

Distributed learning is a problem of fundamental interest in machine learning and cognitive science. In this paper, we present asynchronous distributed learning algorithms for two well-known unsupervised learning frameworks: Latent Dirichlet Allocation (LDA) and Hierarchical Dirichlet Processes (HDP). In the proposed approach, the data are distributed across P processors, and processors indepen...