Shypd.aiThe largest open index of AI toolsAIMET is an open-source library that provides advanced quantization and compression techniques for trained neural network models. It helps improve the runtime performance and reduce the memory footprint of deep learning models for deployment on edge devices.
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AIMET (AI Model Efficiency Toolkit) is an open-source software toolkit developed by Qualcomm Innovation Center, Inc. It specializes in quantizing and compressing trained machine learning models to enhance their runtime performance and reduce memory footprint. This makes models more suitable for deployment on edge devices like mobile phones or laptops. AIMET offers advanced quantization techniques, including Data-Free Quantization (DFQ), AdaRound, and Quantization Aware Training (QAT), to minimize accuracy loss during the optimization process. It also supports model compression techniques like Spatial SVD and Channel Pruning. The toolkit is designed to automate neural network optimization and provides user-friendly APIs for integration into PyTorch pipelines, supporting both ONNX and PyTorch frameworks.
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