Phase Change Memory Drift Compensation in Spiking Neural Networks Using a Non-Linear Current Scaling Strategy
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Bibliographic record
Abstract
The non-ideality aspects of phase change memory (PCM) such as drift and resistance variability can pose significant obstacles in neuromorphic hardware implementations. A unique drift and variability compensation strategy is demonstrated and implemented in an FD-SOI SNN hardware unit composed of embedded phase change memories (ePCMs), current attenuators, and spiking neurons. The effect of drift and variability compensation on inference accuracy is tested on the MNIST dataset to show that our drift and variability mitigation strategy is effective in sustaining its accuracy over time. The variability is reduced by up to 5% while the drift coefficient is reduced by up to 57.8%. The drift is compensated and the SNN classification accuracy is sustained for up to 2 years with intrinsic control-free hardware that tracks the ePCM current over time and consumes less than 30 µW. The results are based on ePCM chip experimental data and pos-layout simulation of a test chip comprising the proposed circuit solution.
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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 | 0.001 |
| 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