Optimized Transformer Models: ℓ′ BERT with CNN-like Pruning and Quantization
Bibliographic record
Abstract
Optimizing techniques for neural network architectures aimed at the edge are complex and intricate, which makes them non-universal. Edge computing and artificial intelligence overlap to enhance data security by enabling data processing at the source, mitigating any risk during data transfer. As data security concerns are growing among world governments, AI on edge has become a highly relevant field of modern research. There is a strict need to harness the power of Convolutional Neural Networks (CNNs) and Transformer networks on resource-constrained edge devices. Although many pruning and quantization techniques have been proposed for CNNs, they may not be directly applied to transformers due to the different computation patterns. This paper will explore the implications of two fundamental techniques: pruning and quantization. We will conduct a comparative analysis to explore the applicability of optimization techniques in Transformers, originally designed for CNNs, for real-world edge deployment. Experimental results show that significant improvement in compression ratio can be achieved while the accuracy of the transformer models is maintained.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".