Summary of An Efficient Matrix Multiplication Algorithm For Accelerating Inference in Binary and Ternary Neural Networks, by Mohsen Dehghankar et al.
An Efficient Matrix Multiplication Algorithm for Accelerating Inference in Binary and Ternary Neural Networks
by Mohsen Dehghankar, Mahdi Erfanian, Abolfazl Asudeh
First submitted to arxiv on: 10 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Data Structures and Algorithms (cs.DS)
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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 proposed paper addresses the inefficiency of Large Language Models (LLMs) by introducing algorithms for improved inference time and memory efficiency. Focusing on matrix multiplication as the bottleneck operation, the authors exploit the fact that pre-trained weight matrices do not change after training. This enables preprocessing and creating indices to reduce storage requirements while enabling efficient inference algorithms. The proposed approach guarantees a time complexity of O(n^2/ln n), a logarithmic factor improvement over standard vector-matrix multiplication. Extensive experiments confirm the superiority of the approach, achieving reductions in inference time up to 29x and memory usage up to 6x. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) are super powerful tools that can understand human language very well. However, they have a big problem: they take too long to make predictions and need lots of computer power. To fix this, scientists came up with new ways to make LLMs work more efficiently. They realized that the weight matrices in these models don’t change much after training, so they can be preprocessed to use less memory and time. This allows for faster and cheaper prediction-making. The researchers tested their ideas and showed that it works really well, making predictions up to 29 times faster and using up to 6 times less computer power. |
Keywords
* Artificial intelligence * Inference
