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ML Suite Release Notes

Release 1.5

New Features

  • Introducing support for Deephi DECENT-TF quantizer (support via Docker container)
  • ML Suite Docker containers for TensorFlow and Caffe
  • TensorFlow Jupyter Notebook
  • TensorFlow Command Line Examples
  • Face detection example
  • YOLOv3 example

Bug fixes

Known Issues

  • Negative weights are not supported for standalone scale/multiply layers

Release 1.4

New Features

  • 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

Bug fixes

Known Issues

Release 1.3

New Features

  • 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

Bug fixes

  • batch_classify example renamed to streaming_classify
  • Improvements to Tensorflow Compiler/Quantizer
  • Improved accuracy for Quantized Resnet101 models
  • Rectangular image support

Known Issues

Release 1.2

New Features

  • 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

Bug fixes

  • 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

Known Issues

Release 1.1

New Features

  • 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_1 for VCU1525 and Nimbix

Bug fixes

  • 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

Known Issues

  • 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

Release 1.0

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.

New Features

  • 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

Known Issues

  • 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)