Abstract

Fine-grained, indefinitely-extended mesh architectures, which can aptly be termed ‘Programmable Matter’, play a complementary rather than competitive role vis-à-vis traditional architectures. Ideal areas of applicability are physical modeling, materials science, interactive modeling of complex objects, image processing, and pattern recognition. Moreover, these architectures are well suited to furnish a viable blueprint for computers built out of atomic-scale components, towards which technology is inexorably leading. In spite of a potentially huge performance gain over conventional computers, programmable matter is currently hard to exploit. In most cases, its raw computational resources do not directly match the structure of problems one might want to apply it to. Since much ad hoc programming is needed to attain a reasonable fraction of the theoretical performance, so far only niche applications have been explored.

We are developing a modeling methodology that will give programmable matter a much broader scope of application. This methodology makes extensive use of synthetic dynamics inspired by physics (kinematic transformations, cellular automata and lattice gases, statistical–mechanical ensembles, simulated annealing, simulated staining, texture-locked loops, etc.), but harnesses these dynamics to data-processing tasks of a more general nature as are encountered in a variety of mundane applications. In particular, by means of suitable feedback, the massive pattern-generation resources of cellular automata machines are used to construct flexible pattern recognizers.

Author Keywords: Fine-grain computation; Parallel computation; Cellular automata; Emergent Coputation