Bio-inspiration helps computers: A new machine
Nicolas Saint-Jean, Gilles Sassatelli, Pascal Benoit, Lionel Torres, Michel Robert · 2008
The past decades have witnessed tremendous research efforts devoted to parallel architectures and programming models for natively computing in space. This resulted in systems which comprise a number of processing units ranging from compact Boolean function generators (FPGAs look-up-tables) to full-fledged microprocessors (MPSoCs). It is often stated in the literature of both areas that performance and/or scalability remain limited by the partial knowledge available at the time the platform is programmed [1] which pushed towards researching techniques granting a certain degree of run-time flexibility to these platforms (partial/ run-time reconfiguration for FPGAs, task migration/load balancing for multiprocessors). This paper presents a bio-inspired machine model which aims at addressing architecture scalability and self-adaptability. The architecture and the programming model are intended to be scalable. The link between the both is based on fully decentralized mechanisms allowing the scalability of the machine and its self-adaptability. An implementation of the proposed bio-inspired machine model has been developed and validated. The preliminary results prove the feasibility and the interest of the approach.