All‐in‐One Analog AI Hardware: On‐Chip Training and Inference with Conductive‐Metal‐Oxide/HfO<sub>x</sub> ReRAM Devices
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Analog in‐memory computing is an emerging paradigm designed to efficiently accelerate deep neural network workloads. Recent advancements have focused on either inference or training acceleration. However, a unified analog in‐memory technology platform–capable of on‐chip training, weight retention, and long‐term inference acceleration–has yet to be reported. This work presents an all‐in‐one analog AI accelerator, combining these capabilities to enable energy‐efficient, continuously adaptable AI systems. The platform leverages an array of analog filamentary conductive‐metal‐oxide (CMO)/HfO x resistive switching memory cells (ReRAM) integrated into the back‐end‐of‐line (BEOL). The array demonstrates reliable resistive switching with voltage amplitudes below 1.5 V, compatible with advanced technology nodes. The array's multi‐bit capability (over 32 stable states) and low programming noise (down to 10 nS) enable a nearly ideal weight transfer process, more than an order of magnitude better than other memristive technologies. Inference performance is validated through matrix‐vector multiplication simulations on a 64 × 64 array, achieving a root‐mean‐square error improvement by a factor of 20 at 1 s and 3 at 10 years after programming, compared to state‐of‐the‐art. Training accuracy closely matching the software equivalent is achieved across different datasets. The CMO/HfO x ReRAM technology lays the foundation for efficient analog systems accelerating both inference and training in deep neural networks.
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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 | 0.001 |
| 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