Memory optimization for packet classification algorithms

We propose novel method how to reduce data structure size for the family of packet classification algorithms at the cost of additional pipelined processing with only small amount of logic resources. The reduction significantly decreases overhead given by the crossproduct nature of classification rules. Therefore the data structure can be compressed to 10% on average. As high compression ratio is achieved, fast on-chip memory can be used to store data structures and hardware architectures can process network traffic at significantly higher speed.