Quantizing Transformers by Helping Attention Heads Do Nothing with Markus Nagel - #663
Published:Dec 26, 2023 20:07
•1 min read
•Practical AI
Analysis
This article summarizes a podcast episode from Practical AI featuring Markus Nagel, a research scientist at Qualcomm AI Research. The primary focus is on Nagel's research presented at NeurIPS 2023, specifically his paper on quantizing Transformers. The core problem addressed is activation quantization issues within the attention mechanism. The discussion also touches upon a comparison between pruning and quantization for model weight compression. Furthermore, the episode covers other research areas from Qualcomm AI Research, including multitask learning, diffusion models, geometric algebra in transformers, and deductive verification of LLM reasoning. The episode provides a broad overview of cutting-edge AI research.
Key Takeaways
- •The podcast episode discusses research on quantizing Transformers to improve efficiency.
- •A key focus is on addressing activation quantization issues within the attention mechanism.
- •The episode also explores the comparison between pruning and quantization for model compression.
Reference
“Markus’ first paper, Quantizable Transformers: Removing Outliers by Helping Attention Heads Do Nothing, focuses on tackling activation quantization issues introduced by the attention mechanism and how to solve them.”