Content-Addressable Memories

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Algorithms for Modern Data Models

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An alternative approach to implementation is based on Superimposed Code Words or Field Encoded Words which are used for more efficient database operations, information retrieval and logic programming, with hardware implementations based on both RAM and head-monitoring disk technology. Ternary CAM TCAM [7] allows a third matching state of "X" or "don't care" for one or more bits in the stored dataword, thus adding flexibility to the search.

For example, a ternary CAM might have a stored word of "10XX0" which will match any of the four search words "", "", "", or "".

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The added search flexibility comes at an additional cost over binary CAM as the internal memory cell must now encode three possible states instead of the two of binary CAM. This additional state is typically implemented by adding a mask bit "care" or "don't care" bit to every memory cell.

CAM Application: Router Address Lookup

Content-addressable memory is often used in computer networking devices. For example, when a network switch receives a data frame from one of its ports, it updates an internal table with the frame's source MAC address and the port it was received on. It then looks up the destination MAC address in the table to determine what port the frame needs to be forwarded to, and sends it out on that port. The MAC address table is usually implemented with a binary CAM so the destination port can be found very quickly, reducing the switch's latency.

Ternary CAMs are often used in network routers , where each address has two parts: the network prefix , which can vary in size depending on the subnet configuration, and the host address, which occupies the remaining bits. Each subnet has a network mask that specifies which bits of the address are the network prefix and which bits are the host address.

Routing is done by consulting a routing table maintained by the router which contains each known destination network prefix, the associated network mask, and the information needed to route packets to that destination. Without CAM, the router compares the destination address of the packet to be routed with each entry in the routing table, performing a logical AND with the network mask and comparing it with the network prefix.

If they are equal, the corresponding routing information is used to forward the packet.


Using a ternary CAM for the routing table makes the lookup process very efficient. The addresses are stored using "don't care" for the host part of the address, so looking up the destination address in the CAM immediately retrieves the correct routing entry; both the masking and comparison are done by the CAM hardware. This works if a the entries are stored in order of decreasing network mask length, and b the hardware returns only the first matching entry; thus, the match with the longest network mask longest prefix match is used.

From Wikipedia, the free encyclopedia. International Symposium on Logic Programming : — Journal of Logic Programming. We believe that this work can open new avenues in very high speed data mining.

Content-addressable memory

Like Bloom filters, our architecture is susceptible to false positives. Surprisingly, we are able to show that the asymptotic false positive probability formula can be safely used if we penalize the small Bloom filter by taking away just one bit of storage and adding just half an extra set element before applying the formula. We believe that this analysis is independently interesting. The subset query problem has applications in databases, network intrusion detection, packet classification in Internet routers, and Information Retrieval.

We demonstrate our architecture on one illustrative streaming application -- intrusion detection in network traffic.

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By shingling the strings in the database, we can perform a single subset query, and hence a single TCAM search, to skip many bytes in the stream. We evaluate our scheme on the open source CLAM anti-virus database, for worst-case as well as random streams. Our architecture appears to be at least one order of magnitude faster than previous approaches. Since the individual Bloom filters must fit in a single TCAM entry currently 72 to bits , our solution applies only when each set is of a small cardinality.

However, this is sufficient for many typical applications. Also, recent algorithms for the subset-query problem use a small-set version as a subroutine.

CSDL | IEEE Computer Society

Social Search at Scale To answer search queries on a social network rich with user-generated content, it is desirable to give a higher ranking to content that is closer to the individual issuing the query. Queries occur at nodes in the network, documents are also created by nodes in the same network, and the goal is to find the document that matches the query and is closest in network distance to the node issuing the query. We believe this is the first feasible solution for the problem of large scale social search.

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Our solution adapts to distributed, real-time implementations on Active Distributed Hash Tables basic distributed stream processing systems with O 1 network calls per search or update. WWW, We assume that the graph of friendships is stored in distributed shared memory, as is the case for large social networks such as Twitter.

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