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qualcomm/aimet: AIMET is a library that provides advanced quantization and compression techniques for trai – Repository qualcomm/aimet. Mô tả: AIMET is a library that provides advanced quantization and compression techniques for trained neural network models.. 2744 stars, 477 forks, ngôn ngữ chính Python, cập nhật 2026-11-09.
1. Repository làm gì?
AIMET is a software toolkit for quantizing trained ML models.
AIMET improves the runtime performance of deep learning models by reducing compute load and memory footprint. Models quantized with AIMET facilitate its deployment on edge devices like mobile phones or laptops by reducing memory footprint.
AIMET employs post-training and fine-tuning techniques to minimize accuracy loss during quantization and compression. AIMET supports models from the ONNX and PyTorch frameworks.
AIMET is designed to work with PyTorch and ONNX models.
You can find models quantized with AIMET on Qualcomm AI Hub Models – a collection of optimized and quantized models.
Please visit the AIMET on Github Pages for more details.
2. Dữ liệu và cấu trúc đáng chú ý
aimet-onnx and aimet-torch is available on PyPI.
Check our Quick Start to get started with latest AIMET package.
To build the latest AIMET code from the source, see Build, install and run AIMET from source in Docker environment
Post-Training Quantization(PTQ)
Check out guide to get started on PTQ technique.
Following table summarizes basic technique such as Calibration to advanced techniques such as SeqMSE and Adaptive Rounding(AdaRound) that you can use with AIMET.
Quantization Aware Training(QAT)
3. Khả năng ứng dụng và giới hạn
AIMET supports Quantization Aware Training(QAT) via aimet-torch.
If you want to use both QAT and some of the advanced PTQ techniques from AIMET, we recommend the following workflow:
AIMET can quantize an existing 32-bit floating-point model to an 8-bit fixed-point model without sacrificing much accuracy and without model fine-tuning.
The DFQ method applied to several popular networks, such as MobileNet-v2 and ResNet-50, result in less than 0.9% loss in accuracy all the way down to 8-bit quantization, in an automated way without any training data.
For this example ADAS object detection model, which was challenging to quantize to 8-bit precision, AdaRound can recover the accuracy to within 1% of the FP32 accuracy.
For some models like the DeepLabv3 semantic segmentation model, AdaRound can even quantize the model weights to 4-bit precision without a significant drop in accuracy.
AIMET supports quantization simulation and quantization-aware training (QAT) for recurrent models (RNN, LSTM, GRU). Using QAT feature in AIMET, a DeepSpeech2 model with bi-directional LSTMs can be quantized to 8-bit precision with minimal drop in accuracy.
AIMET can also significantly compress models. For popular models, such as Resnet-50 and Resnet-18, compression with spatial SVD plus channel pruning achieves 50% MAC (multiply-accumulate) reduction while retaining accuracy within approx. 1% of the original uncompressed model.
Thanks for your interest in contributing to AIMET! Please read our Contributions Page for more information on contributing features or bug fixes. We look forward to your participation!
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