A new algorithm for realization of FIR filters using multiple constant multiplications
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Bibliographic record
Abstract
This paper presents a new common subexpression elimination (CSE) algorithm to realize FIR filters based on multiple constant multiplications (MCMs). This algorithm shares the maximum number of partial terms amongst minimal signed digit (MSD)-represented coefficients. It modifies the iterated matching (ITM) algorithm to share more partial terms in MCMs, which yields a significant logic and, consequently, chip area savings. The employment of the proposed algorithm results in efficient realizations of FIR filters with a fewer number of adders compared to the conventional CSE algorithms. Experimental results demonstrate a reduction up to 22% in the complexity of FIR filters over some conventional CSE algorithms. The proposed algorithm also addresses challenges encountered in resource-constrained applications, which require banks of high-order filters, such as in real-time distributed optical fiber sensor.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it