L-CBF: A Low-Power, Fast Counting Bloom Filter Architecture
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it