- Introducing support for networks with multiple output tensors (Enables SSD)
- Introducing support for Deephi DECENT quantizer (support via Docker container)
- Introducing ML Suite Docker container (replaces use of Anaconda)
- Introducing runtime support for hdf5 files as weight/bias archives
- Updated to support 2018.2 SDx and XRT
- New Platforms Supported:
- VCU1525, Alveo U200, Alveo U250
- Added Support for xDNNv3 - Delivers higher throughput at lower latency
- Auto-detect Platform/DSA
- Both v2 and v3 libxfdnn.so runtime libraries automatically built with
make - Auto-detect xclbin version and correctly load corresponding libxfdnn.so runtime library for XDNN_v2 or XDNN_v3
- Auto-detect batch size based on # PEs (batch size = # PEs, except for xDNNv2 8-bit where it will be double)
- XDNN_v3 max throughput end-to-end demo for Googlenet_v1 and Resnet50
- Customers can use this to enjoy command-line performance that matches FPGA kernel time
- pytest suite to exercise all documented examples/apps
- REST server updated to use new XFDNN API; handles concurrent requests with exec_async
- Perpetual Demo now supports xDNNv3 and upto 8 FPGA cards
- Uptdated xfDNN APIs to allow higher throughput streaming on xDNNv3, and improve ease of use
- xfDNN Compiler enhanced to report better memory utilization and estimate throughput/latency of network
- batch_classify example renamed to streaming_classify
- Improvements to Tensorflow Compiler/Quantizer
- Improved accuracy for Quantized Resnet101 models
- Rectangular image support
- Added support for 2018.2 XRT, and Alveo-U200
- New Jupyter Notebook available:
- Image Classification with TensorFlow
- Benchmark utility script added (GoogLeNet, ResNet50)
- Enhanced Documentation with API markdown guides
- Added Power 9 Support
- Improved layer merging optimizations
- xdnn.computeFC optimized for 2x speedup w/ vectorized numpy implementation
- xdnn.computeSoftmax optimized for 10x speedup w/ vectorized numpy implementation
- In the future we will move to C++ implementation
- Fixed bug where image dimensions mod 32 would cause invalid preprocessing, and zero detections
- Added Jupyter Notebook Support
- New Jupyter Notebooks available:
- Image Classification with Caffe
- Using the xfDNN Compiler w/ a Caffe Model
- Using the xfDNN Quantizer w/ a Caffe Model
- Object Detection w/ YOLOv2 + Darknet to Caffe conversion
- Image Classification Googlenet Demo for VCU1525
- Enhanced Documentation with ML Suite Overview, Overlay Selector Guide and FAQ
- Introducing Nimbix Support
- Updated SDx DSA support to
xilinx_vcu1525_dynamic_5_1for VCU1525 and Nimbix
- AWS overlay names have been updated, removing 'aws' prefix
- General Enhancements and Bug fixes for xfDNN Compiler/Quantizer
- Batch Nom layers implementation corrected from Darknet
- Default file permission fixed
- Root dir issues involving "ml-suite" resolved
- xdnn.execute api no longer needs images/batch argument
- Quantizer updated to allow for custom file names for output files
- xdnn_io updated to return 'none' if there are not FC layers in network
- Batch Normalization layers not supported by the quantizer
- Local Response Normalization layers not supported
- Hardware solution for average pool causes some accuracy loss, to be fixed in a future release
- Standard ReLU is the only supported non-linearity (Leaky ReLU Networks must be modified/retrained
This release is the first push of Xilinx's ML Suite to Github.
Releasing to github will enable rapid release cycles, a smaller release footprint, and open source contribution.
- FPGA Accelerated Image Classification, support for many networks
- FPGA Accelerated Object Detection, YOLOv2 support
- Python API for deploying inference to FPGA
- Precompiled xfDNN Library
- Updated Support for xDNNv2
- Batch Normalization layers not supported by the quantizer
- Local Response Normalization layers not supported
- Hardware solution for average pool causes some accuracy loss, to be fixed in a future release
- Standard ReLU is the only supported non-linearity (Leaky ReLU Networks must be modified/retrained)