Bibliographic record
Abstract
Achieving multi-Gbps clock speeds with static CMOS logic requires migrating designs to smaller geometries, which present a higher development and fabrication cost.Instead, this research investigates the use of clocked CMOS logic to create an inherently pipelined circuit that can be clocked up to 2.5x faster than the standard cells available in the IC design kit.An algorithm was developed and implemented in Perl to process Verilog RTL netlists for compatibility with the clocked logic.As an example application, a cascaded integrator comb (CIC) filter for RF DSP was designed with the clocked CMOS and fabricated in the IBM 130 nm process.Unfortunately, due to an oversight with designing the boundary scan chain, true functionality of the circuit could not be verified.In another demonstration of concept, the algorithm was successfully applied to a QAM modulator design on a Xilinx Virtex-5 FPGA, which achieved a clock speed of 548 MHz.First, I would like to acknowledge my supervisor Prof. Calvin Plett, who supported me throughout this research and provided insightful comments on this thesis.As well, a large thank you goes to Mr. Yatish Kumar, who provided the initial concepts for this research, gave much-needed guidance on the technical issues, and proved to be a valuable mentor to me.I am also grateful to Prof. Ryan Griffin and Mr. Stephen Toth for helping
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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".