Building a Machine Learning Accelerator with Silicon Dangling Bonds: From Verilog to Quantum Dot Layout
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
At a time when traditional CMOS technologies approach their fundamental scaling limits and artificial intelligence continues to escalate global computational demands, emerging post-CMOS technologies like Silicon Dangling Bonds (SiDBs) provide promising pathways towards energy-efficient computation. SiDBs offer atomic-scale precision and discrete charge control, enabling the realization of ultra-dense computational logic. However, manual layout design and verification have historically restricted the exploration and scalability of SiDB-based logic systems. To this end, this work demonstrates an automated, end-to-end Electronic Design Automation (EDA) flow for designing and synthesizing a core component of a Matrix Multiply Unit (MXU) from high-level Register-transfer Level (RTL) Verilog descriptions down to dot-accurate SiDB layouts. Leveraging recent advances in SiDB-focused EDA tooling, we demonstrate the first fully automated design flow capable of translating RTL descriptions into manufacturable quantum-dot layouts. The proposed hierarchical Verilog approach addresses existing EDA constraints while facilitating comprehensive operational verification via test benches. Additionally, our design process incorporates reliability-focused Figures Of Merit (FoMs), ensuring the selection of robust logic gates throughout synthesis. Our synthesized MXU Processing Element (PE) layout represents a significant milestone in SiDB logic design, bridging previously manuallyintensive workflows with scalable, automated methodologies. Despite achieving larger footprints than hand-crafted designs, the presented approach provides a valuable foundation for future optimization and widespread adoption of SiDB-based computing architectures.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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