Charge-trap synaptic device with polycrystalline silicon channel for low power in-memory computing
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
Processing-in-memory (PIM) is gaining tremendous research and commercial interest because of its potential to replace the von Neumann bottleneck in current computing architectures. In this study, we implemented a PIM hardware architecture (circuit) based on the charge-trap flash (CTF) as a synaptic device. The PIM circuit with a CT memory performed exceedingly well by reducing the inference energy in the synapse array. To evaluate the image recognition accuracy, a Visual Geometry Group (VGG)-8 neural network was used for training, using the Canadian Institute for Advanced Research (CIFAR)-10 dataset for off-chip learning applications. In addition to the system accuracy for neuromorphic applications, the energy efficiency, computing efficiency, and latency were closely investigated in the presumably integrated PIM architecture. Simulations that were performed incorporated cycle-to-cycle device variations, synaptic array size, and technology node scaling, along with other hardware-sense considerations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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