AIMET: Advanced Quantization and Compression for Neural Networks

Introduction

AIMET, the AI Model Efficiency Toolkit, is an open-source Python library developed by Qualcomm Innovation Center, Inc. It provides advanced techniques for quantizing and compressing trained neural network models. The primary goal of AIMET is to improve the runtime performance of deep learning models and reduce their memory footprint, making them more efficient for deployment on edge devices like mobile phones or laptops.

AIMET supports models from both the ONNX and PyTorch frameworks. It employs various post-training and fine-tuning techniques to minimize accuracy loss during quantization and compression, ensuring high-performance models without significant degradation. You can find models quantized with AIMET on the Qualcomm AI Hub Models repository.

Installation

Getting started with AIMET is straightforward. The library is available on PyPI, with separate packages for ONNX and PyTorch:

• aimet-onnx: Available on PyPI
• aimet-torch: Available on PyPI

For a quick start guide, refer to the official AIMET Quick Start documentation.

If you prefer to build the latest AIMET code from the source, detailed instructions are available in the Build, install and run AIMET from source in Docker environment guide.

Examples

To see AIMET in action and learn how to integrate it into your workflows, explore the provided examples. You can find example code in the repository's Examples directory. Additionally, the official documentation includes tutorial videos that walk you through various features and use cases.

Why Use AIMET?

AIMET offers compelling advantages for optimizing deep learning models:

• Advanced Quantization Techniques: It enables inference using integer runtimes, which are significantly faster than floating-point runtimes. For instance, models can run 5x-15x faster on Qualcomm Hexagon DSPs. Furthermore, 8-bit precision models have a 4x smaller footprint than 32-bit models. AIMET addresses the challenge of maintaining accuracy during quantization with novel techniques like Data-Free Quantization, delivering state-of-the-art INT8 results on popular models.
• Comprehensive Model Compression: The toolkit supports advanced model compression techniques such as Spatial SVD and Channel Pruning. These methods enable models to run faster at inference time and require less memory, with features like per-layer compression-ratio selection to automate optimization.
• Automated Optimization: AIMET is designed to automate the optimization of neural networks, reducing the need for time-consuming manual adjustments. It provides user-friendly APIs that allow direct integration into PyTorch pipelines.

Links

For more detailed information and community support, refer to these official resources:

• Documentation Main Page
• API Reference
• Discussion Forums
• Slack Channel
• Tutorial Videos