1. Compact State Machines for High Performance Pattern Matching Department of Computer Science and Information Engineering National Cheng Kung University, Taiwan R.O.C. Authors: Piyachon, P. Yan Luo Publisher: Design Automation Conference, 2007. DAC '07. 44th ACM/IEEE 4-8 June 2007 Page(s):493 - 496 Present: Chia-Ming,Chuang Date: 10, 22, 2008 1

2. Outline 1. Introduction 2. State re-rebeling & memory partition 3. Architecture 4. Experiments 5. Conclusion 2

3. Introduction (1/3) References [8]Efficient Memory Utilization on Network Processors for Deep Packet Inspection Authors: Piti Piyachon and Yan Luo Publisher: Architecture for networking and communications systems, Proceedings of the 2006 ACM/IEEE symposium 3

4. Bit-level AC state machine 4 0001 0010 0100 1000 Introduction (2/3)

5. Introduction (3/3) 5

6. Outline 1. Introduction 2. State re-rebeling & memory partition 3. Architecture 4. Experiments 5. Conclusion 6

7. State re-rebeling & memory partition (1/3) re-labeling algorithm 7

8. State re-rebeling & memory partition (2/3) λ is non-zero keywordID vectors 8

9. State re-rebeling & memory partition (3/3) we propose to separate the storage of keywordID matrix from the storage of next-state matrix. Number of k patterns 9

10. Outline 1. Introduction 2. State re-rebeling & memory partition 3. Architecture 4. Experiments 5. Conclusion 10

11. Architecture (1/3) 11

12. Architecture (2/3) 12

13. Outline 1. Introduction 2. State re-rebeling & memory partition 3. Architecture 4. Experiments 5. Conclusion 13

14. Conclusion (1/2) Number of k patterns 14

15. Conclusion (2/2) K*λ K*k 15

16. Outline 1. Introduction 2. State re-rebeling & memory partition 3. Architecture 4. Experiments 5. Conclusion 16

17. Conclusion (1/1) ( ㄧ ) We propose to re-label the states in a state machine such that the matching states are clustered at the beginning of a memory space ( 二 ) The experiment results show significant reduction (up to 80.1%) on the memory consumption of state machines. 17