High-Throughput Low-Energy Self-Timed CAM Based on Reordered Overlapped Search Mechanism
Bibliographic record
Abstract
This paper introduces a reordered overlapped search mechanism for high-throughput low-energy content-addressable memories (CAMs). Most mismatches can be found by searching a few bits of a search word. To lower power dissipation, a word circuit is often divided into two sections that are sequentially searched or even pipelined. Because of this process, most of match lines in the second section are unused. Since searching the last few bits is very fast compared to searching the rest of the bits, we propose to increase throughput by asynchronously initiating second-stage searches on the unused match lines as soon as a first-stage search is complete. In our circuit implementation, each word circuit is independently controlled by a locally generated timing signal rather than a global signal. This allows the circuits to be in the required phase for their own local operation: evaluate or precharge, instead of having to synchronize their phase to the rest of the word circuits, which greatly reduces the cycle time. As a design example, a 128 × 64-bit CAM is implemented and evaluated by HSPICE simulation under a 90 nm CMOS technology. The proposed asynchronous CAM operates 5.98 times faster than a synchronous CAM with 14.2% smaller energy dissipation. The post-layout proposed CAM achieves 385-ps cycle delay time and 0.773 fJ/bit/search and is also evaluated under different corner conditions and PVT variations to guarantee it operates properly.
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 | 0.000 | 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".