Symbolic verification of timed asynchronous hardware protocols

pre-printCorrect interaction of asynchronous protocols re- quires verification. Timed asynchronous protocols add another layer of complexity to the verification challenge. A methodology and automated tool flow have been developed for verifying systems of timed asynchronous circuits through compositional model checking of formal models with symbolic methods. The approach uses relative timing constraints to model timing in asynchronous hardware protocols - a novel mapping of timing into the verification flow. Relative timing constraints are enforced at the interface external to the protocol component. SAT based and BDD based methods are explored employing both interleaving and simultaneous compositions. We present our representation of relative timing constraints, its mapping to a formal model, and results obtained using NuSMV on several moderate sized asynchronous protocol examples. The results show that the capability of previous methods is enhanced to enable the hierarchical verification of substantially larger timed systems

Memory and information processing in neuromorphic systems

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