Power-efficient range-match-based packet classification on FPGA

Yun Rock Qu, Viktor K. Prasanna

Abstract: Packet classification is a kernel application performed at network routers. Many classification engines are optimized for prefix and exact match, while a range-to-prefix translation can lead to rule set expansion. Under limited power budget, it is challenging to achieve high classification throughput. In this paper, we present a high-performance and power-efficient packet classification engine on FPGA. We construct a modular Processing Element (PE); each PE compares a stride of the input packet header against a stride of a range boundary. We concatenate multiple PEs into a systolic array. Efficient power optimization techniques including self-enabled power gating and entropy-based scheduling are explored on our architecture. Experimental results show that, for 4K 15-field rule sets, our prototype on a state-of-the-art FPGA can achieve 250 Million Packets Per Second (MPPS) throughput. Using the proposed power optimization techniques, our classification engine consumes 30% of the power without sacrificing the throughput.