A System-Level Simulator for RRAM-Based Neuromorphic Computing Chips
Why this work is in the frame
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Bibliographic record
Abstract
Advances in non-volatile resistive switching random access memory (RRAM) have made it a promising memory technology with potential applications in low-power and embedded in-memory computing devices owing to a number of advantages such as low-energy consumption, low area cost and good scaling. There have been proposals to employ RRAM in architecting chips for neuromorphic computing and artificial neural networks where matrix-vector multiplication can be computed in the analog domain in a single timestep. However, it is challenging to employ RRAM devices in neuromorphic chips owing to the non-ideal behavior of RRAM. In this article, we propose a cycle-accurate and scalable system-level simulator that can be used to study the effects of using RRAM devices in neuromorphic computing chips. The simulator models a spatial neuromorphic chip architecture containing many neural cores with RRAM crossbars connected via a Network-on-Chip (NoC). We focus on system-level simulation and demonstrate the effectiveness of our simulator in understanding how non-linear RRAM effects such as stuck-at-faults (SAFs), write variability, and random telegraph noise (RTN) can impact an application’s behavior. By using our simulator, we show that RTN and write variability can have adverse effects on an application. Nevertheless, we show that these effects can be mitigated through proper design choices and the implementation of a write-verify scheme.
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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