Bidirectional Programming and its Applications

Many problems in programming involve pairs of computations that cancel out each other’s effects; some examples include parsing/printing, embed- ding/projection, marshalling/unmarshalling, compressing/de-compressing etc. To avoid duplication of effort, the paradigm of bidirectional programming aims at to allow the programmer to write a single program that expresses both computations. Despite being a promising idea, existing studies mainly focus on the view-update problem in databases and its variants; and the impact of bidirectional programming has not reached the wider community. The goal of this thesis is to demonstrate, through concrete language designs and case studies, the relevance of bidirectional programming, in areas of computer science that have not been previously explored. In this thesis, we will argue for the importance of bidirectional programming in programming language design and compiler implementation. As evidence for this, we will propose a technique for incremental refactoring, which relies for its correctness on a bidirectional language and its properties, and devise a framework for implementing program transformations, with bidirectional properties that allow program analyses to be carried out in the transformed program, and have the results reported in the source program. Our applications of bidirectional programming to new areas bring up fresh challenges. This thesis also reflects on the challenges, and studies their impact to the design of bidirectional systems. We will review various design goals, including expressiveness, robustness, updatability, efficiency and easy of use, and show how certain choices, especially regarding updatability, can have significant influence on the effectiveness of bidirectional systems

Dynamically reconfigurable bio-inspired hardware

During the last several years, reconfigurable computing devices have experienced an impressive development in their resource availability, speed, and configurability. Currently, commercial FPGAs offer the possibility of self-reconfiguring by partially modifying their configuration bitstream, providing high architectural flexibility, while guaranteeing high performance. These configurability features have received special interest from computer architects: one can find several reconfigurable coprocessor architectures for cryptographic algorithms, image processing, automotive applications, and different general purpose functions. On the other hand we have bio-inspired hardware, a large research field taking inspiration from living beings in order to design hardware systems, which includes diverse topics: evolvable hardware, neural hardware, cellular automata, and fuzzy hardware, among others. Living beings are well known for their high adaptability to environmental changes, featuring very flexible adaptations at several levels. Bio-inspired hardware systems require such flexibility to be provided by the hardware platform on which the system is implemented. In general, bio-inspired hardware has been implemented on both custom and commercial hardware platforms. These custom platforms are specifically designed for supporting bio-inspired hardware systems, typically featuring special cellular architectures and enhanced reconfigurability capabilities; an example is their partial and dynamic reconfigurability. These aspects are very well appreciated for providing the performance and the high architectural flexibility required by bio-inspired systems. However, the availability and the very high costs of such custom devices make them only accessible to a very few research groups. Even though some commercial FPGAs provide enhanced reconfigurability features such as partial and dynamic reconfiguration, their utilization is still in its early stages and they are not well supported by FPGA vendors, thus making their use difficult to include in existing bio-inspired systems. In this thesis, I present a set of architectures, techniques, and methodologies for benefiting from the configurability advantages of current commercial FPGAs in the design of bio-inspired hardware systems. Among the presented architectures there are neural networks, spiking neuron models, fuzzy systems, cellular automata and random boolean networks. For these architectures, I propose several adaptation techniques for parametric and topological adaptation, such as hebbian learning, evolutionary and co-evolutionary algorithms, and particle swarm optimization. Finally, as case study I consider the implementation of bio-inspired hardware systems in two platforms: YaMoR (Yet another Modular Robot) and ROPES (Reconfigurable Object for Pervasive Systems); the development of both platforms having been co-supervised in the framework of this thesis