A Low Power AI Hardware Accelerator for Microwave-Based Ice Detection
Bibliographic record
Abstract
The fusion of sensors with AI at the edge enables energy-efficient and real-time monitoring and detection. However, very few hardware implementations of edge AI in microwave sensing structures have been reported. In this work, an AI hardware accelerator is presented to empower a microwave-based ice detector, enabling accurate classification of ice, water, and air. The AI accelerator is composed of a current mirror crossbar circuit to perform parallel computations of multiply-and-accumulate operations within the neural network. The proposed circuit design was implemented and simulated in 22 nm FDSOI technology with a power supply of 1.8 V, consuming a low power of$110\ \mu \mathrm{W}$and occupying a small area of$500\ \mu m^{2}$. The AI hardware accelerator achieved a high inference accuracy of 96.5% even with the presence of transistor mismatch variations, compared to the ideal accuracy of 98.8% obtained from the same implementation of the neural network using MATLAB.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".