A Low-Energy Variation-Tolerant Asynchronous TCAM for Network Intrusion Detection Systems
Bibliographic record
Abstract
This paper introduces a low-energy variation-tolerant asynchronous ternary content-addressable memory (TCAM) for Network Intrusion Detection Systems (NIDS). The proposed special-purpose TCAM can detect packet payloads as "virus free" by inspecting only a few bytes. Hence, it adaptively cancels unnecessary searches, leading to greatly reduction in the search delay time and energy dissipation. For timing robustness with low area overhead, a word circuit that stores a virus pattern is designed based on both a quasi-delay insensitive (QDI) and a bundled-data techniques. The QDI word circuit is realized by combining complementary word circuits for only a small portion of the TCAM that is sensitive to delay variations. For performance evaluation, a probability of the virus detection is calculated using a set of real packet traces from MIT DARPA. A 2048 × 128-byte asynchronous TCAM is designed using TSMC 65nm CMOS technology. The energy dissipation is 93.1% lower and the cycle time is 52.4% lower than those of a deep-pipelined synchronous TCAM with a comparable area. It is also demonstrated that the proposed TCAM tolerates up to 47% variations (3s) of threshold voltages.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".