Summary of Quake: Speeding Up Model Inference Using Quick and Approximate Kernels For Exponential Non-linearities, by Sai Kiran Narayanaswami and Gopalakrishnan Srinivasan and Balaraman Ravindran
QuAKE: Speeding up Model Inference Using Quick and Approximate Kernels for Exponential Non-Linearities
by Sai Kiran Narayanaswami, Gopalakrishnan Srinivasan, Balaraman Ravindran
First submitted to arxiv on: 30 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Neural and Evolutionary Computing (cs.NE); Numerical Analysis (math.NA)
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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 authors present QuAKE, a novel collection of operators designed to efficiently approximate exponential functions during machine learning model inference. By leveraging properties of IEEE-754 floating point representations, QuAKE avoids the need for specialized hardware, extra memory, or precomputation, improving computational efficiency by up to 35% on server CPUs and 45% on embedded and mobile-scale CPUs. The proposed optimizations enhance QuAKE’s efficiency in commonly used exponential non-linearities such as Softmax, GELU, and the Logistic function. Evaluations of model performance on standard datasets and tasks from various domains show that QuAKE operators provide sizable speed benefits with little to no loss of performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary QuAKE is a new way to make machine learning models run faster during testing. It’s like a shortcut that helps the computer do calculations more quickly. This is important because as models get bigger and are used more often, they can start to slow down. QuAKE works by using special tricks with how computers store numbers, which allows it to calculate exponential functions (like Softmax) without needing extra help from the hardware or memory. The results show that QuAKE makes a big difference, making calculations 10-35% faster on big computers and 5-45% faster on smaller ones. |
Keywords
» Artificial intelligence » Inference » Machine learning » Softmax
