Computing swarms for self-adaptiveness and self-organizationin floating-point array processing

Advancements in CMOS technology enable the integration of a huge number of resources on the same System-on-Chip. Managing such a growing complexity, including fault tolerance issues in deep sub-micron technologies, is a hard challenge for hardware designers. Self-organization may represent a viable path towards the development of massively parallel architectures in current and future technologies. This approach is progressively more studied in multiprocessor architectures where, however, a further mindset shift in terms of programming paradigm is required. In this paper, self-organization and self-adaptiveness are exploited for the design of a co-processing unit for array computations, supporting floating-point arithmetic. From the experience of previous explorations, an architecture embodying some principle of Swarm Intelligence to pursue adaptability, scalability and fault tolerance is proposed. The architecture realizes a loosely structured collection of hardware agents implementing fixed behavioural rules aimed at the best exploitation of the available resources in whatever kind of context without any hardware reconfiguration. Comparisons with off-the shelf VLIWDSP processors on specific tasks reveal similar performance thus not paying the improved robustness with performance. The multitasking capabilities, together with the intrinsic scalability,make this approach valuable also for future extensions, especially in the field of neuronal networks simulators.