Vitis-AI 2.0 Release (#658)

## Release 2.0
### New Features/Highlights
1. General Availability (GA) for VCK190(Production Silicon), VCK5000(Production Silicon) and U55C
2. Add support for newer Pytorch and Tensorflow version: Pytorch 1.8-1.9, Tensorflow 2.4-2.6
3. Add 22 new models, including Solo, Yolo-X, UltraFast, CLOCs, PSMNet, FairMOT, SESR, DRUNet, SSR as well as 3 NLP models and 2 OFA (Once-for-all) models 
4. Add the new custom OP flow to run models with DPU un-supported OPs with enhancement across quantizer, compiler and runtime 
5. Add more layers and configurations of DPU for VCK190 and DPU for VCK5000
6. Add OFA pruning and TF2 keras support for AI optimizer
7. Run inference directly from Tensorflow (Demo) for cloud DPU


### Release Notes
### Model Zoo
- 22 new models added, 130 total
  - 19 new Pytorch models including 3 NLP and 2 OFA models
  - 3 new Tensorflow models
- Added new application models
  - AD/ADAS: Solo for instance segmentation, Yolo-X for traffic sign detection, UltraFast for lane detection, CLOCs for sensor fusion
  - Medical: SESR for super resolution, DRUNet for image denoise, SSR for spectral remove
  - Smart city and industrial vision: PSMNet for binocular depth estimation, FairMOT for joint detection and Re-ID 
- EoU Enhancements
  - Updated automatic script to search and download required models 

### Quantizer
- TF2 quantizer
  - Add support TF 2.4-2.6
  - Add support for custom OP flow, including shape inference, quantization and dumping
  - Add support for CUDA 11
  - Add support for input_shape assignment when deploying QAT models
  - Improve support for TFOpLambda layers
  - Update support for hardware simulation, including sigmoid layer, leaky_relu layer, global and non-global average pooling layer
  - Bugfixs for sequential models and quantize position adjustment
- TF1 quantizer
  - Add quantization support for new ops, including hard-sigmoid, hard-swish, element-wise multiply ops
  - Add support for replacing normal sigmoid with hard sigmoid
  - Update support for float weights dumping when dumping golden results
  - Bugfixs for inconsistency of python APIs and cli APIs
- Pytorch quantizer
  - Add support for pytorch 1.8 and 1.9
  - Support CUDA 11
  - Support custom OP flow
  - Improve fast finetune performance on memory consumption and accuracy
  - Reduce memory consumption by feature map among quantization
  - Improve QAT functions including better initialization of quantization scale and new API for getting quantizer’s parameters
  - Support more quantization of operations: some 1D and 3D ops, DepthwiseConvTranspose2D, pixel-shuffle, pixel-unshuffle, const
  - Support CONV/BN merging in pattern of CONV+CONCAT+BN
  - Some message enhancement to help user locate problem
  - Bugfixs about consistency with hardware

### Optimizer
- TensorFlow 1.15
  - Support tf.keras.Optimizer for model training
- TensorFlow 2.x
  - Support TensorFlow 2.3-2.6
  - Add iterative pruning
- PyTorch
  - Support PyTorch 1.4-1.9.1
  - Support shared parameters in pruning
  - Add one-step pruning
  - Add once-for-all(OFA)
  - Unified APIs for iterative and one-step pruning
  - Enable pruned model to be used by quantizer
  - Support nn.Conv3d and nn.ConvTranspose3d


### Compiler
- DPU on embedded platforms
   - Support and optimize conv3d, transposedconv3d, upsample3d and upsample2d for DPUCVDX8G(xvDPU)
   - Improve the efficiency of high resolution input for DPUCVDX8G(xvDPU)
   - Support ALUv2 new features
- DPU on Alveo/Cloud
   - Support depthwise-conv2d, h-sigmoid and h-swish for DPUCVDX8H(DPUv4E)
   - Support depthwise-conv2d for DPUCAHX8H(DPUv3E)
   - Support high resolution model inference 
- Support custom OP flow

### AI Library and VART
- Support all the new models in Model Zoo: end-to-end deployment in Vitis AI Library
- Improved GraphRunner to better support custom OP flow
- Add examples on how to integrate custom OPs
- Add more pre-implemented CPU OPs
- DPU driver/runtime update to support Xilinx Device Tree Generator (DTG) for Vivado flow

### AI Profiler
- Support CPU tasks tracking in graph runner
- Better memory bandwidth analysis in text summary
- Better performance to enable the analysis of large models 

### Custom OP Flow
- Provides new capability of deploying models with DPU unsupported OPs
  - Define custom OPs in quantization 
  - Register and implement custom OPs before the deployment by graph runner
- Add two examples
  - Pointpillars Pytorch model
  - MNIST Tensorflow 2 model 

### DPU
- CNN DPU for Zynq SoC / MPSoC, DPUCZDX8G (DPUv2)
  - Upgraded to 2021.2
  - Update interrupt connection in Vivado flow
- CNN DPU for Alveo-HBM, DPUCAHX8H (DPUv3E)
  - Support depth-wise convolution 
  - Support U55C
- CNN DPU for Alveo-DDR, DPUCADF8H (DPUv3Int8)
  - Updated U200/U250 xlcbins with XRT 2021.2
  - Released XO Flow
  - Released IP Product Guide (PG400)
- CNN DPU for Versal, DPUCVDX8G (xvDPU)
  - C32 (32-aie cores for a single batch) and C64 (64-aie cores for a single batch) configurable 
  - Support configurable batch size 1~5 for C64 
  - Support and optimize new OPs: conv3d, transposedconv3d, upsample3d and upsample2d
  - Reduce Conv bubbles and compute redundancy 
  - Support 16-bit const weights in ALUv2
- CNN DPU for Versal, DPUCVDX8H (DPUv4E)
  - Support depth-wise convolution with 6 PE configuration
  - Support h-sigmoid and h-swish

### Whole App Acceleration
- Upgrade to Vitis and Vivado 2021.2
- Custom plugin example: PSMNet using Cost Volume (RTL Based) accelerator on VCK190
- New accelerator for Optical Flow (TV-L1) on U50
- High resolution segmentation application on VCK190
- Options to compare throughput & accuracy between FPGA and CPU Versions
  - Throughput improvements ranging from 25% to 368%
- Reorganized for better usability and visibility

### TVM
- Add support of DPUs for U50 and U55C 

### WeGO (Whole Graph Optimizer) 
- Run inference directly from Tensorflow framework for cloud DPU
  - Automatically perform subgraph partitioning and apply optimization/acceleration for DPU subgraphs
  - Dispatch non-DPU subgraphs to TensorFlow running on CPU
- Resnet50 and Yolov3 demos on VCK5000 

### Inference Server 
- Support xmodel serving in cloud / on-premise (EA)

### Known Issues
- vai_q_caffe hangs when TRAIN and TEST phases point to the same LMDB file
- TVM compiled Inception_v3 model gives low accuracy with DPUCADF8H (DPUv3Int8)
- TensorFlow 1.15 quantizer error in QAT caused by an incorrect pattern match