Hardware implementation of FIR/IIR digital filters using integral stochastic computation
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Bibliographic record
Abstract
Stochastic computing (SC) has received much recent attention due to its inherent fault-tolerance and low implementation cost compared to binary radix representations. SC has been proposed for various signal processing applications such as digital filters. The prior art in stochastic FIR filters can accurately implement the desired filtering function for low-order filters, however, their accuracy degrades as the filter order increases. Moreover, stochastic IIR filters demonstrate high hardware complexity and degraded accuracy. In this paper, we propose an architecture for high-order FIR filters with negligible accuracy loss compared to fixed-point implementation. The proposed architecture requires fewer random number generators. We also describe a novel cascaded second-order direct-form II structure for IIR filters. The implementation results of the proposed design show an improvement in latency and hardware complexity compared to the stochastic architectures reported to date.
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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