Abstract
This letter presents an energy- and memory-efficient pattern-matching engine for a network intrusion detection system (NIDS) in the Internet of Things. Tightly coupled architecture and circuit co-designs are proposed to fully exploit the statistical behaviors of NIDS pattern matching. The proposed engine performs pattern matching in three phases, where the phase-1 prefix matching employs reconfigurable pipelined automata processing to minimize memory footprint without loss of throughput and efficiency. The processing elements utilize 8-T content-addressable memory (CAM) cells for dual-port search by leveraging proposed fixed-1s encoding. A 65-nm prototype demonstrates best-in-class 1.54-fJ energy per search per pattern byte and 0.9-byte memory usage per pattern byte.
Dai Li
PhD 2021, now at Google
