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Filters: Author is Chalamalasetti, Sai Rahul  [Clear All Filters]
2019-08-05
Graves, Catherine E., Ma, Wen, Sheng, Xia, Buchanan, Brent, Zheng, Le, Lam, Si-Ty, Li, Xuema, Chalamalasetti, Sai Rahul, Kiyama, Lennie, Foltin, Martin et al..  2018.  Regular Expression Matching with Memristor TCAMs for Network Security. Proceedings of the 14th IEEE/ACM International Symposium on Nanoscale Architectures. :65–71.

We propose using memristor-based TCAMs (Ternary Content Addressable Memory) to accelerate Regular Expression (RegEx) matching. RegEx matching is a key function in network security, where deep packet inspection finds and filters out malicious actors. However, RegEx matching latency and power can be incredibly high and current proposals are challenged to perform wire-speed matching for large scale rulesets. Our approach dramatically decreases RegEx matching operating power, provides high throughput, and the use of mTCAMs enables novel compression techniques to expand ruleset sizes and allows future exploitation of the multi-state (analog) capabilities of memristors. We fabricated and demonstrated nanoscale memristor TCAM cells. SPICE simulations investigate mTCAM performance at scale and a mTCAM power model at 22nm demonstrates 0.2 fJ/bit/search energy for a 36x400 mTCAM. We further propose a tiled architecture which implements a Snort ruleset and assess the application performance. Compared to a state-of-the-art FPGA approach (2 Gbps,\textbackslashtextasciitilde1W), we show x4 throughput (8 Gbps) at 60% the power (0.62W) before applying standard TCAM power-saving techniques. Our performance comparison improves further when striding (searching multiple characters) is considered, resulting in 47.2 Gbps at 1.3W for our approach compared to 3.9 Gbps at 630mW for the strided FPGA NFA, demonstrating a promising path to wire-speed RegEx matching on large scale rulesets.