Design and evaluation of stochastic FIR filters
Bibliographic record
Abstract
The compact arithmetic units in stochastic computing can potentially lower the implementation cost with respect to silicon area and power consumption. In addition, stochastic computing provides inherent tolerance of transient errors at the cost of a less efficient signal encoding. In this paper, a novel FIR filter design using the stochastic approach based on multiplexers are proposed. The required stochastic sequence length is determined for different signal resolutions by matching the performance of the proposed FIR filter with that of the conventional binary design. Silicon area, power and maximum clock frequency are obtained to evaluate the throughput per area (TPA) and the energy per operation (EPO). For equivalent filtering performance, the stochastic FIR filter underperforms in terms of TPA and EPO compared to the conventional binary design, albeit with some advantages in circuit area and power consumption. The stochastic design, however, shows a graceful degradation in performance with a significant reduction in energy consumption as the stochastic sequences are shortened. The fault-tolerance of the stochastic circuit is compared with that of the binary circuit equipped with triple modular redundancy. It is shown that the stochastic circuit is more reliable than the conventional binary design and its triple modular redundancy (TMR) implementation with unreliable voters, but it is less reliable than the binary TMR implementation when the voters are fault-free.
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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 | 0.001 |
| 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 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".