MemNAS: Super-net Neural Architecture Search for Memristor-based DNN Accelerators
Why this work is in the frame
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Bibliographic record
Abstract
Processing-in-memory (PiM) is an emerging technology that is particularly attractive for deep neural networks (DNN), providing a reduced power consumption at the price of non-idealities in the computations. Furthermore, the PiM platform offers additional parameters to optimize power consumption and non-idealities' effects on computations. However, PiM parameters may need to be tuned differently for each DNN model. In this work, we consider jointly optimizing the DNN architecture and some PiM parameters to improve the Pareto front of DNN performance and power consumption. This paper reports the results of neural architecture search (NAS) experiments on a Once-for-all (OFA) ResNet50 on ImageNet1K and finds that more power efficient DNN models can be identified with the same computational cost as the original OFA method.
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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