OSCAR: An Optical Stochastic Computing AcceleRator for Polynomial Functions
Bibliographic record
Abstract
Approximate computing allows improving design energy efficiency at the cost of computing accuracy. Stochastic computing is an approximate computing technique, where numbers are represented as probabilities using stochastic bit streams. The serial processing of the bit streams leads to reduced hardware complexity but induces high processing latency. Silicon photonics has the potential to overcome this limitation thanks to high propagation speed of signals and high bandwidth. However, the technology remains costly, which calls for optical accelerators capable to adapt to application-specific requirements. In this paper, we propose a reconfigurable optical accelerator capable to adapt to computing accuracy, energy efficiency, and throughput objectives. The architecture can be configured to execute i) 4thorder function for high accuracy processing or ii) 2ndorder function for high-energy efficiency or high throughput purposes. Evaluations are carried out using image processing Gamma correction application. Compared to a static architecture for which accuracy is defined at design time, the proposed architecture leads to 36.8% energy overhead but increases the range of reachable accuracy by 65%.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".