The semantic network array processor (SNAP) is a specialized, highly parallel architecture for knowledge representation and reasoning. The instruction set has been carefully designed to reflect the requirements of semantic network processing. SNAP is a marker propagation architecture, where the passing of markers between cells plays a fundamental role. The movement of markers between cells is controlled by a set of propagation rules. Various reasoning mechanisms were implemented using these propagation rules. A simulator was developed, and knowledge processing examples, such as inheritance, recognition, and classification, were tested. By comparing the simulation results with the same examples run on the Connection Machine, it was found that SNAP outperforms the Connection Machine over a broad range of knowledge processing examples by a factor of 1000 or more.>

The semantic network array processor (SNAP), a highly parallel architecture targeted to artificial intelligence applications, and in particular natural language understanding, is presented. The knowledge is represented in a form of the semantic network. The knowledge base is distributed among the elements of the SNAP array, and the processing is performed locally where the knowledge is stored. A set of powerful instructions specific to knowledge processing is implemented directly in hardware. SNAP is packaged into 256 custom-designed chips assembled on four printed circuit boards and can store a 16 K node semantic network. SNAP is a marker propagation architecture in which the movement of markers between cells is controlled by propagation rules. Various reasoning mechanisms are implemented with these marker propagation rules.>