Energy Efficient TCAM Search Engine Design Using Priority Decision in Memory Technology
ABSTARCT :
Ternary content-addressable memory (TCAM)-based search engines generally need a priority encoder (PE) to select the highest priority match entry for resolving the multiple match problem due to the don’t care (X) features of TCAM. In contemporary network security, TCAM-based search engines are widely used in regular expression matching across multiple packets to protect against attacks, such as by viruses and spam. However, the use of PE results in increased energy consumption for pattern updates and search operations. Instead of using PEs to determine the match, our solution is a three-phase search operation that utilizes the length information of the matched patterns to decide the longest pattern match data. This paper proposes a promising memory technology called priority-decision in memory (PDM), which eliminates the need for PEs and removes restrictions on ordering, implying that patterns can be stored in an arbitrary order without sorting their lengths. Moreover, we present a sequential input-state (SIS) scheme to disable the mass of redundant search operations in state segments on the basis of an analysis distribution of hex signatures in a virus database. Experimental results demonstrate that the PDM-based technology can improve update energy consumption of nonvolatile TCAM (nvTCAM) search engines by 36%–67%, because most of the energy in these search engines is used to reorder. By adopting the SIS-based method to avoid unnecessary search operations in a TCAM array, the search energy reduction is around 64% of nvTCAM search engines.
EXISTING SYSTEM :
? Existing SRAM-based TCAMs on FPGAs suffer from higher energy consumption as they consume excessive power to energize the entire SRAM memory used per lookup.
? In contrast, our proposed work stores the TCAM word’s existence and address information in a single RAM, thus realizing efficient memory usage.
? The existing SRAM-based TCAM architectures energize the entire SRAM memory of their architectures, resulting in excessive power consumption.
? This paper presented a pre-classifier-based architecture for an energy-efficient SRAM-based TCAM, which selectively activates at most one row of SRAM blocks for lookup rather than activating the entire SRAM memory as in the existing architectures.
DISADVANTAGE :
? We address several design issues, including how to get the longest pattern length for the matching entries, how to search the pattern length of matching entries that have the longest pattern length, and how to reduce energy consumption in search operations.
? One challenge in implementing TCAM-based search engines is to identify the energy overhead of search operations that negatively impact system energy.
? However, extra SRAM arrays and comparison circuits are needed to process length comparison for resolving multiple match problems.
? This paper fundamentally tackles the ordering problem and focuses on how to improve energy consumption for the search operation.
PROPOSED SYSTEM :
• The proposed architecture selectively activates at most one row of SRAM blocks for each incoming TCAM word.
• The proposed architecture selectively activates at most one row of SRAM blocks for each incoming TCAM word, thus attaining a substantial reduction in the overall dynamic power consumption.
• This is achieved by partitioning the large width TCAM bit patterns and then implementing them as a cascade of SRAM blocks in the proposed architecture.
• The proposed solution implements TCAM using the embedded SRAM memory blocks available on FPGAs and scales well in terms of speed and power consumption for implementing TCAMS of large storage capacity.
ADVANTAGE :
? We propose an energy-efficient TCAM search engine, employing a clever decision-making process in memory technology for search operations without using PEs.
? In prior studies, the segmented ML scheme is a well-known and very useful technique to efficiently reduce energy consumption in TCAM-based search engines.
? The proposed technique, which decides the highest priority match entry without PEs, is energy efficient.
? We propose an energy-efficient TCAM search engine utilizing TCAM features to get mask length information from the pattern data, and discard the PE in priority-decision in memory (PDM) technology.
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