s a viable product. One answer is that as applications evolve in complexity, in order to guarantee the classification scalability of a systems solution, a systems designer must count on the physical independence of the two modules. Another answer is that because CAMs are still very expensive memory components, but whose prices are rapidly falling, it is sen- sible to allow the volume-based CAM price curve to evolve downwards as was the case with other memory products. This way, users can benefit the most of the advances of this technology as well. 30 29. Misha Nossik, "The Relationship Between Classification Processors and CAMs," Solidum white paper. 30. Ibid. Downloaded from Digital Engineering Library @ McGraw-Hill (www.digitalengineeringlibrary.com) Copyright © 2004 The McGraw-Hill Companies. All rights reserved. Any use is subject to the Terms of Use as given at the website. CLASSIFICATION PROCESSORS 256 NETWORK PROCESSORS INTEGRATED CLASSIFICATION OR STANDALONE? The advantages of using a standalone coprocessor that is fully dedicated to classification, as opposed to programming the classification function as one more major task that runs on a multi-RISC-engine- based NPU, are forcefully and didactically explained and enumerated in Feliks Welfeld's white paper "The Case for a Standalone Classification Processor."31 Welfeld argues that the advantages or disad- vantages of the standalone classification processor must be measured in the speed and size of the code (as the Network Processing Forum [NPF] argues in its definition of a network-processing element) and uses an 8-RISC-core NPU running at 232 MHz on a 1 Gbps link with minimal (512 bits) 64-byte packets as an example. According to this paper, which describes a DiffServ (RFC 2475) implementation in detail (assum- ing purely classification work with no packet modification or encapsulation), 20 percent of the NPU computational capacity goes to classification. With other tasks on the packet, this could easily become 50 percent, especially if variable-size hea