Design Space Exploration of Stochastic Computing Architectures Implemented Using Integrated Optics
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$ \times 4.5$increase in the errors leads to$ \times 47$energy efficiency and$ \times 16$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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".