SCBXP: An Efficient CAM-Based XML Parsing Technique in Hardware Environments
Bibliographic record
Abstract
The underlying technologies of web information and distributed systems often require efficient XML parsing. Even though new software-based XML parsing techniques improve XML processing, the verbose nature of XML does not help to achieve the substantial improvements that are desired. In some systems, such as mobile devices, the restricted memory resources exacerbate the problems associated with XML processing. In this paper, we present a novel XML parsing technique-titled SCBXP-that is designed to achieve high performance in hardware-based environments. In addition, the parsing technique provides a natural way of checking for full well formedness and partial validation, thereby taking advantage of our CAM-based architecture and the inherent parallel features of the hardware. Furthermore, the efficiency of XML parsing is maintained even when memory resources are limited. The SCBXP technique architecture makes use of 1) a content-addressable memory that must be configured with a skeleton of the XML document being parsed, 2) a finite state machine that controls FIFOs, in order to align XML data properly, 3) multiple state machines acting on the multilevel nature of XML, and 4) dual-port memory modules. The results of testing the SCBXP technique, implemented on an FPGA, demonstrate that a processing rate of at least 2 bytes of XML data can be performed during each clock cycle.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".