Characterizing a standard cell library for large scale design of memristive based signal processing
Bibliographic record
Abstract
Abstract In recent years, the use of memristors in circuits design has rapidly increased and attracted research interest. Advances have been made to both the size and the complexity of memristor designs. Therefore, computer aided design tools are required to handle memristor‐based large‐scale designs. A comprehensive automatic framework for the design and synthesis of large‐scale memristor‐complementary metal‐oxide‐semiconductor (CMOS) circuits is described herein. This framework provides a synthesis approach that can be applied to all memristor‐based digital logic designs. In particular, it is a proposal for a characterization methodology of memristor‐based logic cells to generate a standard cell library file for large‐scale simulation. The proposed architecture is based on RRAM and ReRAM redox‐based devices and the memristor ratioed logic design approach. The proposed framework is implemented in the Cadence Virtuoso schematic‐level environment and was verified with Verilog‐XL, MATLAB, and the electronic design automation synopses compiler after being translated to the behavioral level. The proposed method can be applied to implement any digital logic design. Nevertheless, it is perfectly suitable for signal processing applications that require MATLAB functions to produce text files with hex values in order to overcome the limitations of the simulation environment. A framework is deployed herein for design of the memristor‐based parallel 8‐bit adder/subtractor and a 2D memristive‐based median filter. Both proposed designs memristor‐based adder/subtractor and memristive median filter have significant power reductions of 66% and 16% respectively, when compared to the same designs using CMOS technology.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | high |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | high |
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".