Split: Optimizing Space, Power, and Throughput for TCAM-Based Classification

Using Ternary Content Addressable Memories (TCAMs) to perform high-speed packet classication has become the de facto standard in industry because TCAMs facilitate constant time classication by comparing packet elds against ternary encoded rules in parallel. Despite their high speed, TCAMs have limitations of small capacity, large power consumption, and relatively slow access times. One reason TCAM-based packet classiers are so large is the multiplicative eect inherent in representing d-dimensional classiers in TCAMs. To address the multiplicative effect, we propose the TCAM Split architecture, where a d-dimensional classier is split into k = 2 low dimensional classiers, each of which is stored on its own small TCAM. A d-dimensional lookup is split into k low dimensional, pipe-lined lookups with one lookup on each chip. Our experimental results with real-life classiers show that TCAM Split reduces classier size by 84% using only two small TCAM chips, this increases to 93% if we use ve small TCAM chips.