TPU-MLIR v1.8 Release

Highlights:

• Enhancements:

  • Added support for dynamic shape inference in various operations.
  • Optimized core operations for better performance on specific models.
  • Improved backend support for multiple models like BM1684X, BM1688, BM1690, SG2380, etc.
  • Introduced new operations and patterns for more efficient model processing.
  • Updated documentation for better clarity and user guidance.

• Bug Fixes:

  • Resolved issues related to input/output handling, kernel configurations, and model-specific bugs.
  • Fixed bugs in dynamic compilation, core parallel processing, and various backend operations.
  • Addressed errors in specific model post-processing steps like YOLOv5, EfficientNet, etc.

• Performance Improvements:

  • Optimized cycle calculations for multi-core models.
  • Enhanced bandwidth usage statistics for better resource management.
  • Accelerated compilation processes for training models using a new layer-group scheme.

• New Features:

  • Introduced new operations like attention quant block, prelu op, and various dynamic compile features.
  • Added support for additional operations, weight location, and dynamic compile enhancements.

Documentation Updates:

• Updated developer manuals, quick start guides, and model-specific documentation for better understanding.

Miscellaneous:

• Streamlined workflows for faster commit checks and improved debugging processes.
• Added new test cases for regression testing and script-based model evaluations.
• Fine-tuned backend operations for improved model performance and accuracy.