Optics‐Enabled Highly Scalable Inverter for Multi‐Valued Logic
Bibliographic record
Abstract
Abstract The rapid advancements in machine learning have exacerbated the interconnect bottleneck inherent in binary logic‐based computing architectures. An interesting approach to tackle this problem involves increasing the information density per interconnect, i.e., by switching from a two‐valued to a multi‐valued logic (MVL) architecture. However, current MVL implementations offer limited overall performance and face challenges in scaling to process data signals with radix (number of logic levels) even just above 3. In this work, a novel concept is introduced for implementation of a highly scalable and fully passive inverter based on the frequency‐domain phase‐only linear manipulation of the input MVL data signal, which is encoded in the amplitude variations of an electromagnetic wave along the time axis. As a key advantage, this solution is entirely independent of the input radix. The proposed design is implemented using an optical fibre Bragg grating device. Inversion of quaternary signals is experimentally demonstrated, as well as binary and ternary signals, at a remarkable operation speed of 32 GBaud, with an estimated energy consumption of 24 fJ/bit. The proposed method is universal and can be applied to any system that supports transmission and detection of coherent waves, such as microwave, plasmonic, mechanical, or quantum.
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How this classification was reachedexpand
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) | 0.001 | 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 | 0.001 |
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 teacher head, 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".