AI Model Efficiency Toolkit (AIMET)
AIMET, or the AI Model Efficiency Toolkit, is a specialized library designed to enhance and optimize neural network models. By employing advanced quantization and compression techniques, AIMET improves the efficiency of trained models, allowing them to run faster with reduced computational and memory requirements while maintaining accuracy.
Why AIMET?
AIMET supports both advanced quantization and model compression techniques, which help optimize neural networks for quicker inferences with less resource demand. The library enables run-time performance improvements by converting models from floating-point to more efficient integer forms. For instance, Qualcomm's Hexagon DSP can execute 8-bit models up to 15 times faster than its CPU counterpart by leveraging AIMET's quantization capabilities.
Furthermore, AIMET reduces the size of trained models significantly. An 8-bit precision model is four times smaller than a 32-bit precision model. AIMET provides novel methods such as Data-Free Quantization, ensuring high accuracy levels in various popular models during conversion. Additionally, AIMET automates many of these optimization processes, simplifying the implementation for users by offering intuitive APIs for seamless integration with TensorFlow and PyTorch workflows.
Supported Features
Quantization
- Cross-Layer Equalization: Adjusts weight distributions to minimize variations across channels.
- Bias Correction: Ensures accurate layer outputs even after quantization.
- Adaptive Rounding: Finds optimal rounding configurations using unlabeled data.
- Quantization Simulation: Predicts the performance of quantized models.
- Quantization-aware Training: Further trains the model considering its quantized state to boost accuracy.
Model Compression
- Spatial SVD: Splits large layers into smaller segments for optimized processing.
- Channel Pruning: Excises unnecessary channels from layers to streamline weight matrices.
- Per-layer Compression Ratio Selection: Automatically determines optimal compression levels for each layer.
Visualization
- Weight Ranges: Visual analysis of a model's suitability for Cross-Layer Equalization and its effects.
- Per-layer Compression Sensitivity: Provides visual feedback on layer sensitivity to compression effects.
Recent Updates
Recent enhancements involve the introduction of Adaptive Rounding and extending Quantization-aware Training to recurrent models like RNNs, LSTMs, and GRUs.
Results
AIMET is capable of converting 32-bit floating-point models to 8-bit models without extensively retraining or sacrificing accuracy. For example, applying AIMET's DFQ (Data-Free Quantization) method on models like MobileNet-v2 and ResNet-50 results in minimal accuracy loss during 8-bit quantization.
Resources
AIMET provides comprehensive resources to facilitate user engagement, including a detailed User Guide, API Docs, forums for discussion, tutorial videos, and example codes.
Contributions and Team
AIMET encourages community participation, welcoming contributions in the form of features or bug fixes. The project is spearheaded by Qualcomm Innovation Center, Inc., promoting collaborative development.
License
AIMET is available under the BSD 3-Clause License, allowing for wide usage and adaptation in various projects. For more specifics, please refer to the license documentation.