SEVDA: Singular Value Decomposition Based Parallel Write Scheme for Memristive CNN Accelerators
Bibliographic record
Abstract
Von Neumann architecture-based deep neural network architectures are fundamentally bottlenecked by the need to transfer data from memory to compute units. Memristor crossbar-based accelerators overcome this by leveraging Kirchoff's law to perform matrix-vector multiplication (MVM) in-memory. They still, however, are relatively inefficient in their device programming schemes, requiring individual devices to be written sequentially or row-by-row. Parallel writing schemes have recently emerged, which program entire crossbars simultaneously through the outer product of bit-line and word-line voltages and pulse widths respectively. We propose a scheme that leverages singular value decomposition and low-rank approximation to generate all word-line and bit-line vectors needed to program a convolutional neural network (CNN) onto a memristive crossbar-based accelerator. Our scheme reduces programming latency by 90% from row-by-row programming schemes, while maintaining high test accuracy on state of the art image classification models.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".