Release Notes 3.0

Version Compatibility

Vitis AI v3.0 and the DPU IP released with the v3.0 branch of this repository are verified as compatible with Vitis, Vivado™, and PetaLinux version 2022.2. If you are using a previous release of Vitis AI, you should review the version compatibility matrix for that release.

Documentation and Github Repository

  • Migrated core documentation to Github.IO.

  • Incorporated offline HTML documentation for air-gapped users.

  • Restructured user documentation.

  • Restructured repository directory structure for clarity and ease-of-use.

Docker Containers and GPU Support

  • Migrated from multi-framework to per framework Docker containers.

  • Enabled Docker ROCm GPU support for quantization and pruning.

Model Zoo

  • Updated Model Zoo with commentary regarding dataset licensing restrictions

  • Added 14 new models and deprecated 28 models for a total of 130 models

  • Added super resolution 4x, as well as 2D and 3D semantic segmentation for Medical applications

  • Optimized models for benchmarks:

    • MLPerf: 3D-Unet

    • FAMBench: MaskRCNN

  • Provides optimized backbones supporting YoloX, v4, v5, v6 and EfficientNet-Lite

  • Ease-of-use enhancements, including replacing markdown-format performance tables with a downloadable Model Zoo spreadsheet

  • Added 72 PyTorch and TensorFlow models for AMD EPYC™ CPUs, targeting deployment with ZenDNN

  • Added models to support AMD GPU architectures based on ROCm and MLGraphX

TensorFlow 2 CNN Quantizer

  • Based on TensorFlow 2.10

  • Updated the Model Inspector to for improved accuracy of partitioning results expected from the DPU compiler.

  • Added support for datatype conversions for float models, including FP16, BFloat16, FP32, and double.

  • Added support for exporting quantized ONNX format models (to support the ONNX Runtime).

  • Added support for new layer types including SeparableConv2D and PReLU.

  • Added support for unsigned integer quantization.

  • Added support for automatic modification of input shapes for models with variable input shapes.

  • Added support to align the input and output quantize positions for Concat and Pooling layers.

  • Added error codes and improved the readability of the error and warning messages.

  • Various bug fixes.

TensorFlow 1 CNN Quantizer

  • Separated the quantizer code from the TensorFlow code, making it a plug-in module to the official TensorFlow code base.

  • Added support for exporting quantized ONNX format models (to support the ONNX Runtime).

  • Added support for datatype conversions for float models, including FP16, BFloat16, FP32 and double.

  • Added support for additional operations, including Max, Transpose, and DepthToSpace.

  • Added support for aligning input and output quantize positions of Concat and Pooling operations.

  • Added support for automatic replacement of Softmax with DPU-accelerated Softmax.

  • Added error codes and improved the readability of the error and warning messages.

  • Various bug fixes.

PyTorch CNN Quantizer

  • Support PyTorch 1.11 and 1.12.

  • Support exporting torch script format quantized model.

  • QAT supports exporting trained model to ONNX and torch script.

  • Support FP16 model quantization.

  • Optimized Inspector to support more pattern types, and backward compatible of device assignment.

  • Cover more PyTorch operators: more than 560 types of PyTorch operators are supported.

  • Enhanced parsing to support control flow parsing.

  • Enhanced message system with more useful message text.

  • Support fusing and quantization of BatchNorm without affine calculation.

Compiler

  • Added support for new operators, including: strided_slice, cost volume, correlation 1D & 2D, argmax, group conv2d, reduction_max, reduction_mean

  • Added support for Versal™ AIE-ML architectures DPUCV2DX8G (V70 and Versal AI Edge)

  • Focused effort to improve the intelligibility of error and partitioning messages

PyTorch Optimizer

  • Added support for fine-grained model pruning (sparsity)

  • OFA support for convolution layers with kernel sizes = (1,3) and dialation

  • OFA support for ConvTranspose2D

  • Added pruning configuration that allows users to specify pruning hyper-parameters

  • Specific exception types are defined for each type of error

  • Enhanced parallel model analysis with increased robustness

  • Support for PyTorch 1.11 and 1.12

TensorFlow 2 Optimizer

  • Added support for Keras ConvTranspose2D, Conv3D, ConvTranspose3D

  • Added support TFOpLambda operator

  • Added pruning configuration that allows users to specify pruning hyper-parameters

  • Specific exception types are defined for each type of error

  • Added support for TensorFlow 2.10

Runtime and Library

  • Added support for Versal AI Edge VEK280 evaluation kit and Alveo™ V70 accelerator cards (Early Access)

  • Added support for ONNX runtime, with eleven ONNX-specific examples

  • Added four new model libraries to the Vitis™ AI Library and support for fifteen additional models

  • Focused effort to improve the intelligibility of error messages

Profiler

  • Added Profiler support for DPUCV2DX8G (VEK280 Early Access)

  • Added Profiler support for Versal DDR bandwidth profiling

DPU IP - Zynq Ultrascale+ DPUCZDX8G

  • Upgraded to enable Vivado™ and Vitis 2022.2 release

  • Added support for 1D and 2D Correlation, Argmax and Max

  • Reduced resource utilization

  • Timing closure improvements

DPU IP - Versal AIE Targets DPUCVDX8G

  • Upgraded to enable Vivado and Vitis 2022.2 release

  • Added support for 1D and 2D Correlation

  • Added support for Argmax and Max along the channel dimension

  • Added support for Cost-Volume

  • Reduced resource utilization

  • Timing closure improvements

DPU IP - Versal AIE-ML Targets DPUCV2DX8G (Versal AI Edge)

  • Early access release supporting early adopters with an early, unoptimized AIE-ML DPU

  • Supports most 2D operators (currently does not support 3D operators)

  • Batch size support from 1~13

  • Supports more than 90 Model Zoo models

DPU IP - CNN - Alveo Data Center DPUCVDX8H

  • Upgraded to enable Vitis 2022.2 release

  • Timing closure improvements via scripts supplied for .xo workflows

DPU IP - CNN - V70 Data Center DPUCV2DX8G

  • Early access release supporting early adopters with an unoptimized DPU

  • Supports most 2D operators (currently does not support 3D operators)

  • Batch size 13 support

  • Supports more than 70 Model Zoo models

Legacy Alveo DPU Support

  • Vitis AI support for the DPUCAHX8H/DPUCAHX8H-DWC IP, and Alveo™ U50LV and U55C cards was discontinued with the release of Vitis AI 3.0. The final release to support these targets was Vitis AI 2.5.0.

WeGO

  • Integrated WeGO with the Vitis-AI Quantizer to enable on-the-fly quantization and improve easy-of-use

  • Introduced serialization and deserialization with the WeGO flow to offer the capability of building once and running anytime

  • Incorporated AMD ZenDNN into WeGO, enabling additional optimization for AMD EPYC CPU targets

  • Improve WeGO robustness to offer a better developer experience and support a wider range of models

Known Issues

  • Bitstream loading error occurs when the AIE-ML DPU application running on the VEK280 kit is interrupted manually

  • HDMI not functional for the early access VEK280 image. The issue will be fixed in the next release

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