Energy efficient stochastic computing with Sobol sequences
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Bibliographic record
Abstract
Energy efficiency presents a significant challenge for stochastic computing (SC) due to the long random binary bit streams required for accurate computation. In this paper, a type of low discrepancy (LD) sequences, the Sobol sequence, is considered for energy-efficient implementations of SC circuits. The use of Sobol sequences improves the output accuracy of a stochastic circuit with a reduced sequence length compared to the use of another type of LD sequences, the Halton sequence, and conventional linear feedback shift register (LFSR)-generated pseudorandom sequence. The use of Sobol sequences leads to a similar or higher accuracy than using Halton sequences for basic arithmetic operations. Sobol sequence generators cost less energy than the Halton counterparts when multiple random sequences are required in a circuit, thus the use of Sobol sequences can lead to a higher energy efficiency in an SC circuit than using Halton sequences.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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