In-Memory Transformer Self-Attention Mechanism Using Passive Memristor Crossbar
Bibliographic record
Abstract
Transformers have emerged as the state-of-the-art architecture for natural language processing (NLP) and computer vision. However, they are inefficient in both conventional and in-memory computing architectures as doubling their sequence length quadruples their time and memory complexity due to their self-attention mechanism. Traditional methods optimize self-attention using memory-efficient algorithms or approximate methods, such as locality-sensitive hashing (LSH) attention that reduces time and memory complexity from O(L2) to O(L log L). In this work, we propose a hardware-level solution that further improves the computational efficiency of LSH attention by utilizing in-memory computing with semi-passive memristor arrays. We demonstrate that LSH can be performed with low-resolution, energy-efficient 0T1R arrays performing stochastic memristive vector-matrix multiplication (VMM). Using circuit-level simulation, we show our proposed method is feasible as a drop-in approximation in Large Language Models (LLMs) with no degradation in evaluation metrics. Our results set the foundation for future works on computing the entire transformer architecture in-memory.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".