On the Design of Power Attack Immune Spintronic Associative Memory
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
The growing utilization of neural networks has led to a heightened focus on the hardware implementation of such networks. Security concerns associated with these implementations pose a significant challenge in this regard. Among these problems, the vulnerability of these networks against side-channel attacks such as power attacks can be mentioned. This paper presents a technique to enhance the resilience of hardware implementations of neural networks, particularly Hopfield neural networks, to mitigate the risks posed by power attacks. In addition to the fact that the proposed method makes it impossible to attack the network, it also reduces the power consumption of the entire circuit by reducing the leakage currents. The simulation results demonstrate that the proposed approach also achieves about a 10% reduction in energy consumption while concurrently improving the accuracy of the implemented associative memory by 1.1%.
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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