Waveform Transition Graphs: a designer-friendly formalism for asynchronous behaviours

The paper proposes a new formal model for describing asynchronous behaviours involving the interplay of causality, concurrency and choice. The model is called Waveform Transition Graphs. Its main aim is simplifying the learning process for industrial engineers in accessing powerful synthesis tools provided for Signal Transition Graphs by sacrificing some of the expressive power of the latter. This formalism is developed based on feedback from engineers of Dialog Semiconductor.Peer ReviewedPostprint (author's final draft

Automatic synthesis and optimization of partially specified asynchronous systems

A method for automating the synthesis of asynchronous control circuits from high level (CSP-like) and/or partial STG (involving only functionally critical events) specifications is presented. The method solves two key subtasks in this new, more flexible, design flow: handshake expansion, i.e. inserting reset events with maximum concurrency, and event reshuffling under interface and concurrency constraints, by means of concurrency reduction. In doing so, the algorithm optimizes the circuit both for size and performance. Experimental results show a significant increase in the solution space explored when compared to existing CSP-based or STG-based synthesis tools.Peer ReviewedPostprint (author's final draft

A walk in the statistical mechanical formulation of neural networks

Neural networks are nowadays both powerful operational tools (e.g., for pattern recognition, data mining, error correction codes) and complex theoretical models on the focus of scientific investigation. As for the research branch, neural networks are handled and studied by psychologists, neurobiologists, engineers, mathematicians and theoretical physicists. In particular, in theoretical physics, the key instrument for the quantitative analysis of neural networks is statistical mechanics. From this perspective, here, we first review attractor networks: starting from ferromagnets and spin-glass models, we discuss the underlying philosophy and we recover the strand paved by Hopfield, Amit-Gutfreund-Sompolinky. One step forward, we highlight the structural equivalence between Hopfield networks (modeling retrieval) and Boltzmann machines (modeling learning), hence realizing a deep bridge linking two inseparable aspects of biological and robotic spontaneous cognition. As a sideline, in this walk we derive two alternative (with respect to the original Hebb proposal) ways to recover the Hebbian paradigm, stemming from ferromagnets and from spin-glasses, respectively. Further, as these notes are thought of for an Engineering audience, we highlight also the mappings between ferromagnets and operational amplifiers and between antiferromagnets and flip-flops (as neural networks -built by op-amp and flip-flops- are particular spin-glasses and the latter are indeed combinations of ferromagnets and antiferromagnets), hoping that such a bridge plays as a concrete prescription to capture the beauty of robotics from the statistical mechanical perspective.Comment: Contribute to the proceeding of the conference: NCTA 2014. Contains 12 pages,7 figure

An Iterative Receiver for OFDM With Sparsity-Based Parametric Channel Estimation

In this work we design a receiver that iteratively passes soft information between the channel estimation and data decoding stages. The receiver incorporates sparsity-based parametric channel estimation. State-of-the-art sparsity-based iterative receivers simplify the channel estimation problem by restricting the multipath delays to a grid. Our receiver does not impose such a restriction. As a result it does not suffer from the leakage effect, which destroys sparsity. Communication at near capacity rates in high SNR requires a large modulation order. Due to the close proximity of modulation symbols in such systems, the grid-based approximation is of insufficient accuracy. We show numerically that a state-of-the-art iterative receiver with grid-based sparse channel estimation exhibits a bit-error-rate floor in the high SNR regime. On the contrary, our receiver performs very close to the perfect channel state information bound for all SNR values. We also demonstrate both theoretically and numerically that parametric channel estimation works well in dense channels, i.e., when the number of multipath components is large and each individual component cannot be resolved.Comment: Major revision, accepted for IEEE Transactions on Signal Processin

State encoding of asynchronous controllers using pseudo-boolean optimization

State encoding of asynchronous controllers is a challenging problem that faces a vast space of solutions. Subtle differences in the insertion of signals may result in significant variations in the complexity of the logic. This paper proposes a novel approach that models the encoding problem as Pseudo-Boolean formula. A cost function that estimates the complexity of the logic is incorporated, where the estimator of essential literals becomes one of the most important terms of the function. The new approach has been tested in 175 benchmarks with encoding conflicts, including 127 four-phase latch controllers. The presence of logic estimators in the formula contributes to an average reduction of 43% in literals when compared to a plain SAT version of the problem, at the expense of a longer runtime. When comparing to the region-based approach in petrify, an average reduction of 14% in literals is obtained.Peer ReviewedPostprint (author's final draft

Synthesis of asynchronous controllers using integer linear programming

A novel strategy for the logic synthesis of asynchronous control circuits is presented. It is based on the structural theory of Petri nets and integer linear programming. Techniques that are capable of checking implementability conditions, such as complete state coding, and deriving a gate netlist to implement the specified behavior are presented. These techniques can handle Petri net specifications consisting of several thousands of transitions and provide a significant speed-up compared with techniques that have previously been proposed.Peer ReviewedPostprint (published version

CayleyNets: Graph Convolutional Neural Networks with Complex Rational Spectral Filters

The rise of graph-structured data such as social networks, regulatory networks, citation graphs, and functional brain networks, in combination with resounding success of deep learning in various applications, has brought the interest in generalizing deep learning models to non-Euclidean domains. In this paper, we introduce a new spectral domain convolutional architecture for deep learning on graphs. The core ingredient of our model is a new class of parametric rational complex functions (Cayley polynomials) allowing to efficiently compute spectral filters on graphs that specialize on frequency bands of interest. Our model generates rich spectral filters that are localized in space, scales linearly with the size of the input data for sparsely-connected graphs, and can handle different constructions of Laplacian operators. Extensive experimental results show the superior performance of our approach, in comparison to other spectral domain convolutional architectures, on spectral image classification, community detection, vertex classification and matrix completion tasks

Message sequence chart specifications with cross verification

Current software specification verification methods are usually performed within the context of the specification method. There is little cross verification, pitting one type of specification against another, taking place. The most common techniques involve syntax checks across specifications or doing specification transformations and running verification within the new context. Since viewpoints of a system are different even within programming teams we concentrate on producing an efficient way to run cross verification on specifications, particularly specifications written with Message Sequence Charts and State Transition Diagrams.;In this work an algorithm is proposed in which all conditional MSCs are transformed into an algebraic representations, Message Flow Graphs and by stepwise refinement, a Global State Transition Graph is created. This GSTG has all the properties of a State Transition Diagram and therefore can be analyzed in conjunction with the original STD