Photonic Convolution Engine Based on Phase-Change Materials and Stochastic Computing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The last wave of AI developments sparked a global surge in computing resources allocated to neural network models. Even though such models solve complex problems, their mathematical foundations are simple, with the multiply-accumulate (MAC) operation standing out as one of the most important. However, improvements in traditional CMOS technologies fail to match the ever-increasing performance requirements of AI applications, therefore new technologies, as well as disruptive computing architectures must be explored. In this paper, we propose a novel in-memory implementation of a MAC operator based on stochastic computing and optical phase-change memories (oPCMs), leveraging their proven non-volatility and multi-level capabilities to achieve convolution. We show that resorting to the stochastic computing paradigm allows one to exploit the dynamic mechanisms of oPCMs to naturally compute and store MAC results with less noise sensitivity. Under similar conditions, we demonstrate an improvement of up to $64\times$ and $10\times$ in the applications that we evaluated.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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