An Overview of Hardware Implementations of P Systems

Abstract

Implementing the P systems on parallel hardware is a re- search highlight in bio-inspired computing since the membrane comput- ing is a large-scale parallel computing paradigm which have a potential to tremendously speed up the computation. Field-programmable gate arrays (FPGAs) and CUDA-enabled GPUs are the primary hardware which is employed to implement P systems. FPGA-based hardware im- plementations use different strategies considering regions or evolution rules as processing units. This implies the existence of several parallel architectures for FPGAs specially designed to implement P systems. In contrast, the CUDA-enabled GPUs are a pre-defined parallel platform and numerous types of P systems are directly implemented on it. The object distribution problem (choosing which rules will be applied) is the core problem of all hardware implementations. This problem is par- ticularly difficult, because in the general case the model of P systems is non-deterministic and maximally parallel, hence the corresponding prob- lem is NP-hard. Several heuristics were proposed in order to accelerate the process of the computation of the corresponding ruleset. In this article we overview different approaches and designs for hardware implementations of P systems as well as corresponding solutions to the object assignment problem