DNN-Based Optimization to Significantly Speed Up and Increase the Accuracy of Electronic Circuit Design
Bibliographic record
Abstract
Efficient design and optimization of flip-flops can significantly affect overall circuit performance as they have many applications in digital systems which can impact the overall power consumption and timings of the emerging system on chips (SOCs). In this paper, modeling, design, and optimization of transmission gate-based master-slave positive-edge-triggered flip-flop (TGFF) in 16 nm complementary metal-oxide semiconductor (CMOS) is proposed. The proposed deep neural network (DNN)-based optimization method first generates an accurate model for different performance metrics by using the training data obtained from transistor-level models which are over 100 times faster than them. Then, these accurate DNN-based models are used to optimize design goals such as dynamic and static power, setup time, and propagation delay (Data to Output). Using these fast, accurate models significantly speed up the design procedure and leads to a considerably more optimized design. Additionally, as the DNN is a universal approximator that can catch any nonlinear input-output relationship, the proposed method can be used to optimize circuits for any performance metric, even if no analytical formula is available. Additionally, circuit design based on the proposed method is automated which, facilitates the tasks of circuit designers.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".