Sharpness-Aware Minimization Scaled by Outlier Normalization for Robust DNNs on In-Memory Computing Accelerators
Bibliographic record
Abstract
Many deep neural network (DNN) models consume a significant amount of energy at inference time, in large part due to energy consumed by memory access. In-memory computing addresses this problem by eliminating many memory accesses, but exposes model weights to noise and circuit variations. While several methods have been proposed to train DNNs robust to weight noise they typically require knowledge of the noise distribution, or degrade the DNN performance in noiseless setting. In this work, we first show that applying sharpness-aware training, by optimizing for both the loss value and loss sharpness, significantly improves robustness to noisy weights at inference time. Then, we propose a new adaptive sharpness-aware method that conditions the worst-case perturbation of a given weight not only on its magnitude but also on the range of the weight distribution. This is achieved by performing sharpness-aware minimization scaled by outlier normalization (SAMSON). Results on computer-vision benchmarks show that SAMSON increases model robustness to noisy weights without compromising generalization performance in noiseless regimes.
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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) | 0.000 | 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 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".