Summary of Beexformer: a Fast Inferencing Transformer Architecture Via Binarization with Multiple Early Exits, by Wazib Ansar et al.
BEExformer: A Fast Inferencing Transformer Architecture via Binarization with Multiple Early Exits
by Wazib Ansar, Saptarsi Goswami, Amlan Chakrabarti
First submitted to arxiv on: 6 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 proposes Binarized Early Exit Transformer (BEExformer), a novel architecture that combines early exit with binarization for textual inference, addressing issues in model deployment on devices with constrained resources. The BEExformer improves the binarization process through differentiable second-order approximation and enables gradient computation concerning both sign and magnitude of weights. It achieves improved accuracy by 5.98% and reduces FLOPs during inference by 54.85%. Additionally, it simplifies training without requiring knowledge distillation from a full-precision LLM. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new type of machine learning model that can be used on devices with limited resources. This model is called the Binarized Early Exit Transformer (BEExformer). It’s special because it can combine two things that are usually done separately: making the model smaller and using an early exit strategy. The BEExformer uses a new way to approximate the impulse function, which helps with gradient computation. This means it can be trained without needing a full-precision model as a teacher. The paper shows that this new model is better than others in its class, with improved accuracy and reduced processing power needed. |
Keywords
» Artificial intelligence » Inference » Knowledge distillation » Machine learning » Precision » Transformer
