L-CBF: A Low-Power, Fast Counting Bloom Filter Architecture
Bibliographic record
Abstract
We study the energy, latency and area characteristics of two Counting Bloom Filter implementations using full custom layouts in a commercial 0.13μm technology. The first implementation, S-CBF, uses an SRAM array of counts and a shared counter. The second, L-CBF, utilizes an array of up/down linear feedback shift registers. Circuit level simulations demonstrate that for a 1K-entry CBF with a 15-bit count per entry, L-CBF is 3.7 or 1.6 times faster than the S-CBF depending on the operation. The L-CBF requires 2.3 or 1.4 times less energy per operation compared to the S-CBF. However, the L-CBF requires 3.2 times more area. We demonstrate that for one application of CBFs (early hit/miss detection for L1 caches [12] for an aggressive dynamically-scheduled superscalar processor) the energy consumed by the L-CBF is 60% of the energy consumed by the S-CBF for most of the SPEC CPU 2000 benchmarks.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".