Summary of Ai and Memory Wall, by Amir Gholami et al.
AI and Memory Wall
by Amir Gholami, Zhewei Yao, Sehoon Kim, Coleman Hooper, Michael W. Mahoney, Kurt Keutzer
First submitted to arxiv on: 21 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Hardware Architecture (cs.AR); Distributed, Parallel, and Cluster Computing (cs.DC)
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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 discusses the limitations of large language models (LLMs) in terms of their massive size and compute requirements, which are being fueled by unprecedented amounts of unsupervised training data. However, it argues that the primary bottleneck is shifting from computation to memory bandwidth. The authors show how this limitation affects decoder Transformer models specifically, highlighting the need for a redesign of model architecture, training, and deployment strategies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper talks about big language models getting bigger and needing more computer power to train. But they’re running into a problem: their memory isn’t keeping up with the growth of their compute needs. This is especially true for decoding models that need to store lots of information. To solve this issue, we might need to rethink how these models are designed, trained, and used. |
Keywords
* Artificial intelligence * Decoder * Transformer * Unsupervised
