Stochastic circuit design and performance evaluation of vector quantization
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Bibliographic record
Abstract
Vector quantization (VQ) is a general data compression technique that has a scalable implementation complexity and potentially a high compression ratio. In this paper, a novel implementation of VQ using stochastic circuits is proposed and its performance is evaluated. The stochastic and binary designs are compared for the same compression quality and the circuits are synthesized for an industrial 28-nm cell library. The effects of varying the sequence length of the stochastic design are studied with respect to the performance metric of throughput per area (TPA). When a shortened 512-bit encoding sequence is used to obtain a lower quality compression, the TPA is about 2.60 times that of the binary implementation with the same quality as that of the stochastic implementation measured by the L <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> norm error (i.e., the first-order error). Thus, the stochastic implementation outperforms the conventional binary design in terms of TPA for a relatively low compression quality. By exploiting the progressive precision feature of a stochastic circuit, a readily scalable processing quality can be attained by simply halting the computation after different numbers of clock cycles.
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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 | 0.001 | 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