Introduction to Bolt
Bolt is a lightweight library designed for deep learning. Developed with the intent to simplify and speed up the deployment of neural networks, Bolt stands out as an excellent tool for automating deployment pipelines and achieving exceptional acceleration rates. It has gained popularity and extensive use within numerous departments at Huawei, where it is trusted for various AI tasks.
Why Choose Bolt?
Bolt is designed with several key advantages that make it an essential tool for developers and researchers working with neural networks:
- High Performance: Bolt is over 15% faster compared to existing open-source acceleration libraries, ensuring rapid processing of neural networks.
- Rich Model Conversion: It supports conversions for a variety of frameworks, including Caffe, ONNX, TFLite, and TensorFlow, allowing for seamless integration and flexibility.
- Various Inference Precision: Bolt supports multiple precision levels such as FP32, FP16, INT8, and even 1-BIT, giving users options based on their precision needs.
- Multiple Platforms: It is versatile with support across various platforms, including ARM CPU (v7, v8, v8.2+, v9), X86 CPU (with AVX2 and AVX512 support), as well as GPUs from Mali, Qualcomm, Intel, and AMD.
- Broad Application Support: Bolt is notably the first to support NLP applications and is versatile enough for common computer vision tasks as well.
- Efficient Resource Management: It is designed to minimize ROM/RAM usage.
- Advanced Optimization Tools: Includes rich graph optimization and efficient thread affinity settings.
- Additional Features: Supports auto algorithm tuning and time-series data acceleration for further efficiency and speed.
Building Status
For those looking to delve into Bolt, it offers compatibility with a range of platforms and devices. Depending on the environment and specific needs, developers can tailor the build process, adding options for on-device training or multi-thread parallel executions using openMP.
Quick Start with Bolt
Getting started with Bolt is straightforward, requiring just two key steps:
- Model Conversion: Use the X2bolt tool to convert models from Caffe, ONNX, TFLite, or TensorFlow into Bolt’s own .bolt file format.
- Inference Execution: Run the benchmark tool using the .bolt file alongside data to obtain inference results. More detailed instructions are available in the user handbook.
Applications of Bolt
Bolt isn’t just theoretical; it’s practical, with applications spanning several fascinating domains:
- Image Classification and Face Detection tools are available for Android and iOS.
- For more specialized tasks, there's Pose Detection, Semantics Analysis, Reading Comprehension, and even Speech Recognition.
Detailed tutorials and examples are available for each application, ensuring users can quickly integrate Bolt into their own systems.
Verified Networks
Bolt has proven its capabilities across a range of networks:
- Computer Vision (CV): Popular models like ResNet50, EfficientNet, and YOLO versions.
- Natural Language Processing (NLP): Modern models like Bert, GPT-2, and Roberta.
- Recommendation Systems: Models including NFM and DeepFM have verified Bolt’s performance.
In essence, Bolt emerges as a flexible and highly efficient tool for developers aiming to push the boundaries of what is possible with neural networks across various fields, including computer vision, natural language processing, and recommendation systems.