On the power implications of floating point addition in IIR filters
Why this work is in the frame
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Bibliographic record
Abstract
In floating point DSP applications, filtering of data samples is one of the most demanding operations. While the specification driven filter design delivers transfer functions satisfying target applications, certain implementations of these transfer functions can result in filters exhibiting various undesirable artifacts. This work addresses the characterization of the relative power implications of floating point Direct form II and Transposed Direct form II IIR filters. In programmable DSP applications, (compared to Transposed Direct form II realizations), Direct form II realizations offers better power reduction as far as the power implications of the floating point adder segment of DSPs are concerned. During filtering of white noise samples, the alignment driven data path switchings of transposed realizations had been found to be around 3 to 4 times that of direct form realizations. Performance of the same experiment involving audio samples substantiates the above findings.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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