This paper presents a novel iterative approach to achieve effective and efficient approximate logic synthesis (ALS). The core idea is to perform circuit rewriting in a way that is both local, i.e., is applied piece-wise to selected subcircuits, and extensive, i.e., systematically explores the design space for good solutions. Concretely, we propose SubXPAT, a new Boolean rewriting framework which iteratively employs satisfiability modulo theories (SMT) solving to select and approximate key parts of a circuit. Selection aims at finding subcircuits that at the same time include a significant number of gates and can be efficiently approximated, which is done by searching for large convex subcircuits with a limited number of inputs and outputs. Approximation is guided by the use of a parametric template, structured as a sum of products, which allows for fine-grained control over the subcircuit characteristics. SubXPAT was implemented as an open-source tool and compared against other ALS tools implementing state-of-the-art techniques. Our experimental evaluation used a broad range of arithmetic circuits with different bit-widths and our results indicate that SubXPAT generates approximate circuits that are more area-efficient than those generated by state-of-the-art techniques in 72% of the cases.

BACKGROUND: Vaccines development in this millennium started by the milestone work on Neisseria meningitidis B, reporting the invention of Reverse Vaccinology (RV), which allows to identify vaccine candidates (VCs) by screening bacterial pathogens genome or proteome through computational analyses. When NERVE (New Enhanced RV Environment), the first RV software integrating tools to perform the selection of VCs, was released, it prompted further development in the field. However, the problem-solving potential of most, if not all, RV programs is still largely unexploited by experimental vaccinologists that impaired by somehow difficult interfaces, requiring bioinformatic skills. RESULTS: We report here on the development and release of NERVE 2.0 (available at: https://nerve-bio.org ) which keeps the original integrative and modular approach of NERVE, while showing higher predictive performance than its previous version and other web-RV programs (Vaxign and Vaxijen). We renewed some of its modules and added innovative ones, such as Loop-Razor, to recover fragments of promising vaccine candidates or Epitope Prediction for the epitope prediction binding affinities and population coverage. Along with two newly built AI (Artificial Intelligence)-based models: ESPAAN and Virulent. To improve user-friendliness, NERVE was shifted to a tutored, web-based interface, with a noSQL-database to consent the user to submit, obtain and retrieve analysis results at any moment. CONCLUSIONS: With its redesigned and updated environment, NERVE 2.0 allows customisable and refinable bacterial protein vaccine analyses to all different kinds of users.