v1.15-beta.0

feat: make lmem assignment stage more analyzable

- define some commonly used LOG macro (Logger.h)
- define some strinify function to show lmem type and timestep mode
  (LayerGroupDefs.h)
- add show_timestep_table to print readable timestep table
  (BasicTimeStep.h/BasicTimeStep.cpp)
- add many DEBUG_WITH_TYPE logs and comments in lmem assignment stage
  (BasicTimeStep.cpp/LmemAllocator.cpp/TimeStepMethod.cpp/SwPipeline.cpp)
- rename some variables and function names for better represent the
  process(gen_all_mem_buffer_ts/tgt_min_address/...)
- reduce assignLmemAddr cyclomatic complexity.(LmemAllocator.cpp:989)

Change-Id: I31dadb9424be334da481f9dfbd45985ca89dc058

v1.14

[doc] refine user interface

Change-Id: I887ad481b2a3b1f7dce4fe993399ec2afa093bb4

v1.14-beta.0

fix bug in build ppl

Change-Id: Ib93341da7fa6b420f9fb9cd9e4b61dc21aeaf001

v1.13

add a16 matmul multi_core

Change-Id: I10a9097ee52e324555f4a505ce18d7fe9b665803

v1.13-beta.0

[doc] layergroup opt intro

Change-Id: I0797b73e4d020e9556da29d1c1a743b8c80a83ad

v1.12

Features

• Support for backend operators implemented using PPL.
• TPUv7-runtime CModel integrated with TPU-MLIR for BM1690 model CModel inference.
• Optimized inference speed for BM1690 Stable Diffusion 3.0 at 512 resolution to 0.72 img/s (Mac utilization: 41.9%).
• Support for training graph compilation of ResNet50-v1 through FxGraphConverter.

Bug Fixes

• Performance: Fixed the issue of performance degradation in SegNet.
• Functionality: Resolved the compilation comparison issue for BM1688 DeppLabv3P.

Known Issues

• Performance: Slight performance degradation observed in BM1690 YOLOv5-6 with 4 batch INT8 on eight cores.

v1.12-beta.0

combine slice and concate to new Rope ConcatToRope

Change-Id: Ib15b12fe97117b96c6fe7267c96c3f714aac6ec4

v1.11

[python] distinguish data path model-zoo from regression

Change-Id: I98fa0df1524f0b38d91cda02ab5d49876f7caee8
(cherry picked from commit fa082d0b29df8a82af77839df86349aabab86949)

v1.11-beta.0

[soc_dump] add doc

Change-Id: Icaf313113415a9bf0ad9c75abdcb609d661c815b

TPU-MLIR v1.10 Release

Release Note

Enhancements:

• Added CUDA support for various operations like conv2d, MatMul, dwconv, pool2d, and more.
• Improved performance for operations like MeanStdScale and softmax.
• Enhanced multi-core batch mm and added support for bm168x with CUDA.
• Refined CUDA code style and adjusted interfaces for various operations.

Bug Fixes:

• Fixed issues with matmul, calibration failures, conv pad problems, and various performance problems.
• Addressed bugs in model transformations, calibration, and various pattern issues.
• Resolved bugs in different model backends like ssd, vit, detr, and yolov5.

New Features:

• Added support for new models like resnet50, mobilenet_v2, shufflenet_v2, and yolox_s/alphapose_res50.
• Introduced new operations like RequantIntAxisOp and Depth2Space with CUDA support.
• Implemented new functionalities for better model inference and compilation.

Documentation Updates:

• Updated weight.md, calibration sections, and user interface details.
• Improved documentation for quick start, developer manual, and various tpulang interfaces.
• Enhanced documentation for model transformation parameters and tensor data arrangements.

Miscellaneous:

• Added new npz tools, modelzoo regression, and support for bmodel encryption.
• Fixed issues with various model performance, shape inference, and CUDA backend optimizations.
• Revived performance for models like yolov5s-6, bm1690 swin multicore, and more.