Synthesis and Optimization of Reversible Circuits - A Survey

Reversible logic circuits have been historically motivated by theoretical research in low-power electronics as well as practical improvement of bit-manipulation transforms in cryptography and computer graphics. Recently, reversible circuits have attracted interest as components of quantum algorithms, as well as in photonic and nano-computing technologies where some switching devices offer no signal gain. Research in generating reversible logic distinguishes between circuit synthesis, post-synthesis optimization, and technology mapping. In this survey, we review algorithmic paradigms --- search-based, cycle-based, transformation-based, and BDD-based --- as well as specific algorithms for reversible synthesis, both exact and heuristic. We conclude the survey by outlining key open challenges in synthesis of reversible and quantum logic, as well as most common misconceptions.Comment: 34 pages, 15 figures, 2 table

Digital service analysis and design : the role of process modelling

Digital libraries are evolving from content-centric systems to person-centric systems. Emergent services are interactive and multidimensional, associated systems multi-tiered and distributed. A holistic perspective is essential to their effective analysis and design, for beyond technical considerations, there are complex social, economic, organisational, and ergonomic requirements and relationships to consider. Such a perspective cannot be gained without direct user involvement, yet evidence suggests that development teams may be failing to effectively engage with users, relying on requirements derived from anecdotal evidence or prior experience. In such instances, there is a risk that services might be well designed, but functionally useless. This paper highlights the role of process modelling in gaining such perspective. Process modelling challenges, approaches, and success factors are considered, discussed with reference to a recent evaluation of usability and usefulness of a UK National Health Service (NHS) digital library. Reflecting on lessons learnt, recommendations are made regarding appropriate process modelling approach and application

Characterizing the Shape of Activation Space in Deep Neural Networks

The representations learned by deep neural networks are difficult to interpret in part due to their large parameter space and the complexities introduced by their multi-layer structure. We introduce a method for computing persistent homology over the graphical activation structure of neural networks, which provides access to the task-relevant substructures activated throughout the network for a given input. This topological perspective provides unique insights into the distributed representations encoded by neural networks in terms of the shape of their activation structures. We demonstrate the value of this approach by showing an alternative explanation for the existence of adversarial examples. By studying the topology of network activations across multiple architectures and datasets, we find that adversarial perturbations do not add activations that target the semantic structure of the adversarial class as previously hypothesized. Rather, adversarial examples are explainable as alterations to the dominant activation structures induced by the original image, suggesting the class representations learned by deep networks are problematically sparse on the input space