Using Recurrent Neural Networks To Learn The Structure Of Interconnection Networks
Abbreviated Journal Title
INTERCONNECTION NETWORKS; RECURRENT NETWORKS; REAL-TIME TRAINING; KNOWLEDGE EXTRACTION; SEQUENTIAL MACHINES; FINITE-STATE AUTOMATA; ARCHITECTURE; COMPLEXITY; INDUCTION; Computer Science, Artificial Intelligence
A modified Recurrent Neural Network (RNN) is used to learn a Self-Routing Interconnection Network (SRIN) from a set of routing examples. The RNN is modified so that it has several distinct initial states. This is equivalent to a single RNN learning multiple different synchronous sequential machines. We define such a sequential machine structure as augmented and show that a SRIN is essentially an Augmented Synchronous Sequential Machine (ASSM). As an example, we learn a small six-switch SRIN. After training we extract the network's internal representation of the ASSM and corresponding SRIN.
"Using Recurrent Neural Networks To Learn The Structure Of Interconnection Networks" (1995). Faculty Bibliography 1990s. 1346.