Efficient distributed arithmetic based DWT architecture for multimedia applications
Bibliographic record
Abstract
This paper presents a novel architecture for 9/ Discrete Wavelet Transform (DWT) based on Distributed Arithmetic (DA). The proposed architecture optimizes the performance by exploiting the computational redundancy. The DWT inner product of coefficient matrix is distributed over the input by careful analysis of input, output and coefficients word lengths. In the coefficient matrix, linear maps are used to assign the necessary computation processing elements in space domain. The result is a low hardware complexity DWT processor for 9/7 transforms, which allows two times faster clock than the direct implementation. In the proposed architecture reducing the clock frequency by two or the supply voltage and maintaining the same throughput as of other architecture achieve the low power by a factor of four. The proposed architecture is therefore scalable and can operate at high speed / consumes low power and has reduced computational complexity (improvement of 77.6% over filter based and 40.27% over lifted based architectures) as compared to already published 9/7 biorthogonal wavelet architectures.
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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 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".