Design Space Exploration of Stochastic Computing Architectures Implemented Using Integrated Optics
Why this work is in the frame
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Bibliographic record
Abstract
Approximate computing allows to trade-off design energy efficiency with computing accuracy. Stochastic computing is an approximate computing technique, where numbers are represented as bit streams corresponding to probabilities. The serial computation of the bit streams leads to reduced hardware complexity but involves high latency, which is the main limitation of the technique. Integrated optics technology relies on high propagation speed of signals, which has the potential to reduce the processing latency in stochastic computing. However, the design of stochastic computing architectures implemented using integrated optics involves the exploration of numerous parameters at system and technological levels. In this work, we propose a design space exploration framework that allows to optimize energy efficiency, computing accuracy, and latency of such architectures. The efficiency of the framework is evaluated using a Gamma correction image processing application. Results show that, for processing 160 x 160 pixels images, an acceptable <inline-formula><tex-math notation="LaTeX">$ \times 4.5$</tex-math></inline-formula> increase in the errors leads to <inline-formula><tex-math notation="LaTeX">$ \times 47$</tex-math></inline-formula> energy efficiency and <inline-formula><tex-math notation="LaTeX">$ \times 16$</tex-math></inline-formula> processing speed. We also show that the same computing accuracy can be obtained for different energy efficiency and computing latency, thus, validating the ability of the framework to explore the design space.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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