What's New in Vitis™ AI

Vitis AI 1.4

Vitis AI 1.4 Release Highlights

  • Support new platforms, including Kria KV260 SoM kit and Versal ACAP platforms VCK190, VCK5000; 
  • Extended Pytorch framework support from version 1.5 to version 1.7.1;
  • Added new state-of-the-art models, including 4D Radar detection, Image-Lidar sensor fusion, 3D detection & segmentation, multi-task, depth estimation, super resolution and more models that applicable to automotive, smart medical, industrial vision applications;
  • Easier subgraph partition user experience with the new Graph Runner API;
  • Improved performance;

Vitis AI 1.4 What’s New by Category

Expand the sections below to learn more about the new features and enhancements in Vitis AI 1.4.

  1. Added 16 new models, and total 108 models from different deep learning frameworks (Caffe, TensorFlow, TensorFlow 2 and PyTorch) are provided.
  2. Increased the diversity of models compared to Vitis AI 1.3:
    1. For autonomous driving and ADAS, added 4D Radar detection, Image-Lidar sensor fusion, surround-view 3D detection, upgraded 3D segmentation and multi-task models
    2. For medical and industrial vision, added depth estimation, RGB-D segmentation, super-resolution and other reference models
  3. EoU enhancement: provided automated download scripts for free selection of the versions according to model name and hardware platform
  1. Support fast finetune in post-training quantization (PTQ);
  2. Improved quantize-aware training(QAT) functions:
  3. Support more layers: swish/sigmoid, hard-swish, hard-sigmoid, LeakyRelu, nested tf.keras functional and sequential models
  4. Support more layers:
    1. swish/sigmoid, hard-swish, hard-sigmoid, LeakyRelu
    2. Nested tf.keras functional and sequential models
  5. Support new models: EfficientNet, EfficientNetLite, Mobilenetv3, Yolov3 and Tiny Yolov3
  6. Support custom layers via subclassing tf.keras.layers and support custom quantization strategies
  7. Support custom layers and support custom quantization strategies
  8. Improved ease-of-use and bug fixed


  1. Support Pytorch 1.5-1.7.1
  2. Support activations
    1. hard-swish, hard-sigmoid
  3. Support more operators:  
    1. Const, Upsample, etc.
  4. Support shared parameters in quantization
  5. Enhanced quantization profiling and error check functions
  6. Improved QAT functions:
    1. support training from PTQ results
    2. support reused modules
    3. support resuming training
  1. Support tf.keras APIs in TF1
  2. Supports single GPU mode for model analysis 
  1. Improved easy-of-use with simplified APIs;
  2. Support torch.nn.ConvTranspose2d;
  3. Support reused modules;
  1. Support ALU for DPUCVDX8G (xvDPU)
  2. Support cross-layer prefetch optimization option
  3. Support xmodel output nodes assignment
  4. Enabled features to implement zero-copy for:
    1. DPUCZDX8G (DPUv2)
    2. DPUCAHX8H (DPUv3E)
    3. DPUCAHX8L (DPUv3ME)
  5. Open-sourced network visualization tool Netron officially supports Xilinx XIR 
  1. Support the 16 new models in AI Model Zoo:
    1. 11 new Pytorch models
    2. 5 new Tensorflow models, 1 from Tensorflow 2.x
    3. 1 new Caffe models
  2. Introduced new deploy APIs graph_runner, especially for models with multiple subgraphs
  3. Introduced new tool xdputil for DPU and xmodel debug
  4. Support new KV260 SoM kit
  5. Support DPUCVDX8G (xvDPU) on VCK190
  6. DPUCVDX8H (DPUv4E) on VCK5000
  1. Support Versal platforms VCK190 and VCK5000
  2. Support Petalinux 2021.1, OpenCV v4 
    1. EoU improved by updating the samples to use INT8 as input, reduced the conversion from FP32 to INT8;
  1. Support new DPU IPs:
    1. DPUCVDX8G (xvDPU)
    2. DPUCAHX8L (DPUv3ME)
    3. DPUCVDX8H (DPUv4E)
  2. Support DPUv2 & xvDPU in vivado flow
  3. Memory IO statistics
  4. EoUs improved 
  1. DPUv2 IP upgraded to 2021.1
  1. VCK190 xvDPU TRD
  2. Support batch size 1~6 which is configurable based on C32 mode
  3. PL support new OPs:
    1. Global Average Pooling up to 256x256, Element Multiply, Hardsigmoid and Hardswish
  4. More models deployed 
  1. Release xo in Vitis AI 1.4
  1. Support latest U250 platform (2020.2) 
  2. Support latest U200 platform (2021.1)
  3. Bug fixed
  1. Improved the DPU performance of small networks processing with weight pre-fetch function
  1. Multi Object Tracking (SORT) example on ZCU102 provided
  2. Classification App example for Versal (VCK190) provided
  3. Updated existing examples to XRT APIs and zero copy
  4. U200 (DPUv3INT8) TRD provided
  5. Ported U200/250 examples to use DPUv3INT8 instead of DPUv1
  6. Example for xRNN pre-processing acceleration (embedding layer)
  7. SSD MobileNet U280 example now accelerates both pre and post-processing on hardware
  1. Support of all DPUs - ZCU102/4, U50, U200, U250, U280
  2. Using Petalinux for edge devices
  3. Increased throughput using AKS at the application level
  4. Yolov3 tutorial as python notebook
  1. Unified DPU kernels into one and added samples for Alveo U200/250 (DPUv3INT8), U280, U50, U50lv