Membrane-based design and management methodology for parallel dynamically reconfigurable embedded systems

International audiencePartial and dynamic reconfiguration provides a relevant new dimension to design efficient parallel embedded systems. However, due to the encasing complexity of such systems, ensuring the consistency and parallelism management at runtime is still a key challenge. So architecture models and design methodology are required to allow for efficient component reuse and hardware reconfiguration management.This paper presents a distributed persistence management model and its implementation for reconfigurable multiprocessor systems on dynamically reconfigurable circuits. The proposed approach is inspired from the well-known component based models used in software applications development. Our model is based on membranes wrapping the systems components. The objective is to improve design productivity and ensure consistency by managing context switching and storage using modular distributed hardware controllers. These membranes are distributed and optimized with the aim to design self-adaptive systems by allowing dynamic changes in parallelism degree and contexts migration. Simulation and synthesis results are given to show performances and effectiveness of our methodology

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

A Survey on FPGA-Based Sensor Systems: Towards Intelligent and Reconfigurable Low-Power Sensors for Computer Vision, Control and Signal Processing

The current trend in the evolution of sensor systems seeks ways to provide more accuracy and resolution, while at the same time decreasing the size and power consumption. The use of Field Programmable Gate Arrays (FPGAs) provides specific reprogrammable hardware technology that can be properly exploited to obtain a reconfigurable sensor system. This adaptation capability enables the implementation of complex applications using the partial reconfigurability at a very low-power consumption. For highly demanding tasks FPGAs have been favored due to the high efficiency provided by their architectural flexibility (parallelism, on-chip memory, etc.), reconfigurability and superb performance in the development of algorithms. FPGAs have improved the performance of sensor systems and have triggered a clear increase in their use in new fields of application. A new generation of smarter, reconfigurable and lower power consumption sensors is being developed in Spain based on FPGAs. In this paper, a review of these developments is presented, describing as well the FPGA technologies employed by the different research groups and providing an overview of future research within this field.The research leading to these results has received funding from the Spanish Government and European FEDER funds (DPI2012-32390), the Valencia Regional Government (PROMETEO/2013/085) and the University of Alicante (GRE12-17)

Hardware architectures for neural processing systems for bio-inspired robot control

This PhD Thesis is focussed in the study of spiking neural networks. In this framework the presented work presents different hardware architectures that are implemented in reconfigurable devices (FPGAs). Different approaches are proposed adopting time¬driven or alternatively event-driven processing schemes. The work presents alternative control approaches in the field of robotics and studies computing architectures for the simulation of massive spiking neural networks of millions of neurons processing sensorimotor information in real-time. These proposed approaches have been implemented in two hybrid Hardware/Software platforms with different levels of autonomy of the hardware (stand-alone and co-processing strategy) with respect to the software modules (in a PC as a host computer) that simulated in real-time these large scale networks. In a second stage, this Thesis focuses on experiments with real-robots, as validation methodology of the control neural networks under study. The choice of working with real robots instead of simulated ones in motivated by the difficulty of describing in a realistic way the interaction with the real-world in a simulated framework. Therefore, the work here also adopts the "Embodiment concept" which stresses the necessity of having a physical body as learning mechanism for the knowledge emergence generation. In this field, the Thesis describes two robotic platforms built and adapted for being controlled by spiking neural systems. The obtained results show that imitating in more or less detail the biology is feasible building neural circuits which represent valid alternatives to be considered for control of biomorphic robots with complex physical structures