AI Model Efficiency Toolkit (AIMET Spatial) SVD

Join our experts from Qualcomm AI Research in this tutorial on using the Spatial SVD model compression feature within the AI Model Efficiency Toolkit (AIMET). This session is helpful for developers and researchers looking to reduce the computational load and memory usage of their machine learning models without compromising on performance.

In this video, you will explore:

Spatial SVD Tensor Decomposition Technique - Gain an in-depth understanding of how this technique works to compress convolutional layers by decomposing them into two smaller layers, effectively reducing model size and complexity.

Automatic Compression Ratio Selection - Learn about the innovative approach to determining optimal compression levels for each layer using a technique known as greedy compression ratio selection, which involves multiple evaluations to ensure efficiency.

Greedy Compression Ratio Selection Technique - Dive deeper into the specifics of this method, including the step-by-step process of per-layer exploration and the application of a greedy search to finalize compression ratios.

This tutorial is designed to equip you with the skills needed to implement advanced model compression strategies effectively. If you're looking to enhance your understanding of model efficiency tools and apply them in real-world scenarios, this video is a must-watch.

Thank you for watching! Get started with AIMET today: https://www.qualcomm.com/developer/software/ai-model-efficiency-toolkit

AIMET Github: https://github.com/quic/aimet

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Видео AI Model Efficiency Toolkit (AIMET Spatial) SVD канала Qualcomm Developer