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(L<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>) 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 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".