Three new neural network based algorithms for IP lookup and packet classification

Routers use look-up tables to forward packets. They also classify packets to deterinine which flow they belong to and what quality of service (QoS) they should receive. Increasing the rate of communication links is in contrast to the practical processing power of routers and switches. We propose some neural network algorithms to solve the IP lookup problem. One of these algorithms, back propagation, gives promising results; however, it has problems in training time. Another algorithm, a 12 layer neural network, represents acceptable results on error rate and training time. Parallel processing of neural networks provides huge processing power to do IP lookup. The algorithm can be implemented in hardware on a single chip. Our method can perform an IP lookup in 4.5 nanoseconds, which implies the support for a 60 Gbps link rate. Pipelining and parallel processing can be used to increase the link rate up to 400 Gbps and also decrease the teaming time.