Bibliographic record
Abstract
With the increasing effect of on-chip interconnects on nowadays [sic] VLSI design performance, modeling of interconnects becomes a necessity. GAM, TPN, and AWE are well known methods that are used to map an interconnect to an equivalent electrical circuit. In this thesis, a general approach that considers z-parameters is developed witch allows the generation of equivalent RC, RLC, and RLCG circuits for both T and ∏ configurations. The performance of these generated circuits is compared to H-spice simulations by measuring the effect of interconnects on the transition times and delays under different conditions such as input transition times, interconnect lengths and capacitive loads. As a result, the a-configuration of AWE method reveals consistently an acceptable performance which makes it a good candidate to be utilized for buffer insertion.Buffer insertion is a popular technique used to reduce the delay of a long interconnect by segmenting it and inserting buffers among these segments. Therefore, the performance of this technique depends strongly on the accuracy of the considered interconnect model. However, using a model such as the RLCG of ∏ configuration which is derived from using the AWE method is not practical due to the complexity accompanied by such model which makes the derivation of closed-form expressions very complicated. To overcome this dilemma, the selected configuration has been mapped to a simple equivalent RC circuit. As a consequence, a new RC representation of on-chip interconnects is developed. Moreover, depending on the developed RC model, the proposed buffer insertion technique shows superiority over previously published works.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".