Summary of Hybrid Dynamic Pruning: a Pathway to Efficient Transformer Inference, by Ghadeer Jaradat et al.
Hybrid Dynamic Pruning: A Pathway to Efficient Transformer Inference
by Ghadeer Jaradat, Mohammed Tolba, Ghada Alsuhli, Hani Saleh, Mahmoud Al-Qutayri, Thanos Stouraitis, Baker Mohammad
First submitted to arxiv on: 17 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces Hybrid Dynamic Pruning (HDP), a novel approach to accelerate Transformer models for deployment on edge devices. This is crucial as Transformers are highly computationally intense and memory-hungry, making them challenging to deploy in real-time applications. HDP combines algorithm-architecture co-design with pruning techniques to reduce computations in attention and memory access. Specifically, the authors propose row-balanced block pruning and head pruning to detect and prune unimportant blocks and heads at runtime. Additionally, an approximation method is introduced to further reduce attention computations. To support these methods, a HDP co-processor architecture is proposed, aiming for lower latency and power efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Transformers are powerful AI models that help us understand language and recognize images. However, they’re not very good at running on devices like smartphones or smart home devices because they use too much energy and take too long to process information. To solve this problem, scientists developed a new way to make Transformers work better on edge devices. They created an algorithm called Hybrid Dynamic Pruning (HDP) that makes the model more efficient by getting rid of parts that are not important. This helps reduce the amount of energy needed and makes the processing faster. |
Keywords
* Artificial intelligence * Attention * Pruning * Transformer
