This paper introduces a new datapath architecture for reconfigurable processors. The proposed datapath is based on Network-on-Chip approach and facilitates tight coupling of all functional units. Reconfigurable functional elements can be dynamically allocated for application specific optimizations, enabling polymorphic computing. Using a modified network simulator, performance of several NoC topologies and parameters are investigated with standard benchmark programs, including fine grain and coarse grain computations. Simulation results highlight the flexibility and scalability of the proposed polymorphic NoC processor for a wide range of application domains.
The discovery of non-linear systems dynamics has impacted concepts of knowledge to ascribe to it dynamic properties. It has expanded a development that finds its roots more than hundred years ago. Then, certainty was sought in systems of scientific insight. Such absolute certainty was inevitably static as it would be irrevocable once acquired. Although principal limits to the obtainability of knowledge were defined by scientific and philosophical advances from the 1920s through the mid-twentieth century, the knowledge accessible within those boundaries was considered certain, allowing detailed description and prediction within the recognized limits. The trend shifted away from static theories of knowledge with the discovery of the laws of nature underlying non-linear dynamics. The gnoseology of complex systems has built on insights of non-periodic flow and emergent processes to explain the underpinnings of generation and destruction of information and to unify deterministic and indeterministic descriptions of the world. It has thus opened new opportunities for the discourse of doing research.