We are working on an algorithm to optimize the logic circuits that can be realized on the super fine-grain parallel processing architecture. As a part of this work, we have developed an inverter reduction algorithm. This algorithm is based on modeling logic circuits as dynamical systems. We implement the algorithm in the PARTHENON system, which is the high level synthesis system developed in NTT's laboratories, and evaluate it using ISCAS85 benchmarks. We also compare the results with both the existing algorithm of PARTHENON and the algorithm of Jain and Bryant.

This paper introduces a flexible, stream-oriented dataflow processing model based on the "Communicating Logic (CL)" framework. As the target architecture, we adopt the dual layered "Plastic Cell Architecture (PCA). " Datapath processing functionality is encapsulated in asynchronous hardware objects with variable graining and implemented using look-up tables. Communication (i.e. connectivity and control) between the distributed processing objects is achieved by means of inter-object message passing. The key point of the CL approach is that it offers the merits of scalable performance, low power hardware implementation with the user friendly compilation and linking capabilities unique to software.

We propose an architectural reference of programmable devices that we call Plastic Cell Architecture (PCA). PCA is a reference for implementing a device with autonomous reconfigurability, which we also introduce in this paper. This reconfigurability is a further step toward new reconfigurable computing, which introduces variable- and programmable-grained parallelism to wired logic computing. This computing follows the Object-Oriented paradigm: it regards configured circuits as objects. These objects will be described in a new hardware description language dealing with the semantics of dynamic module instantiation. PCA is the fusion of SRAM-based FPGAs and cellular automata (CA), where the CA are dedicated to support run time activities of objects. This paper mainly focus on autonomous reconfigurability and PCA. The following discussions examine a research direction towards general-purpose reconfigurable computing.