Optimizing Neural Networks for Mobile Deployment
Techniques for reducing model size and latency while maintaining accuracy for mobile AI applications.
2024-02-05
10 min read
Practical approaches to deploying and optimizing AI models for production environments.
Techniques for reducing model size and latency while maintaining accuracy for mobile AI applications.
Exploring INT8, FP16, and dynamic quantization methods for efficient inference on resource-constrained devices.
Best practices for converting models to ONNX format and optimizing them for cross-platform deployment.