A novel energy efficient 4-bit vedic multiplier using modified GDI approach at 32 nm technology
Bibliographic record
Abstract
Multipliers are essential components within digital signal processing, arithmetic operations, and various computational tasks, making their design and optimization crucial for improving the efficiency and performance of integrated circuits. Among multiplier architectures, Vedic multipliers stand out due to their inherent efficiency and speed, derived from ancient Indian mathematical principles. This study presents a comprehensive analysis and comparison of 4-bit Vedic multiplier designs utilizing Gate Diffusion Input (GDI), Complementary Metal-Oxide-Semiconductor (CMOS), and Transmission Gate (TG) technologies, utilizing different adder architectures such as Ripple Carry Adder (RCA), and Carry Lookahead Adder (CLA), Carry Skip Adder (CSA). The objective is to explore the performance, area, and power consumption characteristics of these multipliers across different technologies and adder implementations. Each multiplier architecture is meticulously designed and optimized to leverage the unique features of the respective technology while adhering to the principles of Vedic mathematics. The designs are evaluated based on parameters such as transistor count, delay, power dissipation, and area. The results demonstrate the effectiveness of GDI technology in terms of in tems of delay, area, power and PDP when compared with other technologies. The 4-bit Vedic multiplier has been designed using 32 nm technology within Tanner EDA software tools.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".