FPGA AI Suite: IP Reference Manual

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ID 768974
Date 12/16/2024
Public
Document Table of Contents
Document Table of Contents x
1. FPGA AI Suite IP Reference Manual 2. About the FPGA AI Suite IP 3. FPGA AI Suite IP Generation Utility 4. FPGA AI Suite Ahead-of-Time Splitter Utility 5. CSR Map and Descriptor Queue A. FPGA AI Suite IP Reference Manual Archives B. FPGA AI Suite IP Reference Manual Document Revision History
2. About the FPGA AI Suite IP x
2.1. Supported Models 2.2. Model Performance 2.3. Software Emulation of the FPGA AI Suite IP 2.4. FPGA AI Suite Layer / Primitive Ranges 2.5. FPGA AI Suite IP Block Configuration 2.6. IP Block Interfaces 2.7. Feature Input and Output Streaming 2.8. DDR-Free Operation
2.1. Supported Models x
2.1.1. MobileNet V2 differences between Caffe and TensorFlow models
2.2. Model Performance x
2.2.1. Throughput on the MobileNetV1 model (and other very fast models)
2.5. FPGA AI Suite IP Block Configuration x
2.5.1. Architecture Description File Format for Instance Parameterization 2.5.2. Architecture Description File Parameters
2.5.2. Architecture Description File Parameters x
2.5.2.1. Parameter Group: Global Parameters 2.5.2.2. Parameter Group: activation 2.5.2.3. Parameter Group: pe_array 2.5.2.4. Parameter Group: pool 2.5.2.5. Parameter Group: depthwise 2.5.2.6. Module: softmax 2.5.2.7. Parameter Group: dma 2.5.2.8. Parameter Group: xbar 2.5.2.9. Parameter Group: filter_scratchpad 2.5.2.10. Parameter Group: input_stream_interface 2.5.2.11. Parameter Group: output_stream_interface 2.5.2.12. Parameter Group: config_network 2.5.2.13. Parameter Group: layout_transform_params
2.6. IP Block Interfaces x
2.6.1. Clock and Reset 2.6.2. AXI Interfaces 2.6.3. AXI Interface Clock and Reset 2.6.4. Input Feature Tensor In-Memory Format 2.6.5. Output Tensor In-Memory Format
2.6.4. Input Feature Tensor In-Memory Format x
2.6.4.1. Multiple Input Graphs 2.6.4.2. Input Folding 2.6.4.3. Input Scale and Shift 2.6.4.4. Input Transform Mapping 2.6.4.5. Input Layout Transform Hardware
2.7. Feature Input and Output Streaming x
2.7.1. Input Streaming 2.7.2. Output Streaming
3. FPGA AI Suite IP Generation Utility x
3.1. IP Generation Utility Execution Flows 3.2. IP Generation Utility Inputs 3.3. IP Generation Utility Outputs 3.4. IP Generation Utility Command Line Options
3.4. IP Generation Utility Command Line Options x
3.4.1. The --flow create_ip Flow 3.4.2. The --flow add_arch Flow 3.4.3. The --flow list Flow 3.4.4. The --flow remove_arch Flow
4. FPGA AI Suite Ahead-of-Time Splitter Utility x
4.1. Files Generated by the FPGA AI Suite Ahead-of-Time (AOT) Splitter Utility 4.2. Building the FPGA AI Suite Ahead-of-Time (AOT) Splitter Utility 4.3. Running the FPGA AI Suite Ahead-of-Time (AOT) Splitter Utility 4.4. FPGA AI Suite Ahead-of-Time (AOT) Splitter Utility Example Application
5. CSR Map and Descriptor Queue x
5.1. Discovery ROM 5.2. Interrupt Control 5.3. DMA Descriptor Queue 5.4. DMA Control Registers 5.5. Performance Registers 5.6. Debug Network Registers 5.7. DMA License Register 5.8. DMA Transaction Counters
1. FPGA AI Suite IP Reference Manual
2. About the FPGA AI Suite IP
3. FPGA AI Suite IP Generation Utility
4. FPGA AI Suite Ahead-of-Time Splitter Utility
5. CSR Map and Descriptor Queue
A. FPGA AI Suite IP Reference Manual Archives
B. FPGA AI Suite IP Reference Manual Document Revision History

4.3. Running the FPGA AI Suite Ahead-of-Time (AOT) Splitter Utility

Before you run the FPGA AI Suite AOT splitter utility, you must have the following files ready:
  • A model compiled by the dla_compiler command. The model must target the HETERO:FPGA format and the output format must be open_vino_hetero.
    For example, the following command compiles a model that targets a board with an Agilex™ 7 device to a file called RN50_Performance_b1.bin: 
    cd $COREDLA_ROOT/demo/models/public/resnet-50-tf/FP32
dla_compiler \
  --march $COREDLA_ROOT/example_architectures/AGX7_Performance.arch \
  --network-file ./resnet-50-tf.xml \
  --foutput-format=open_vino_hetero \
  --o $COREDLA_ROOT/demo/RN50_Performance_b1.bin \
  --b=1 \
  --fanalyze-performance
  • The input or inputs for model inference.

The AOT splitter utility outputs the artifacts to the current working directory. The command line options must have a compiled model, model inputs, and a path to the splitter utility plugins XML file.

To run the AOT splitter utility:

  1. Change directories to where you want the output files from the AOT splitter utility to go.
  2. Run the splitter utility command as follows: 
    $COREDLA_ROOT/runtime/build_Release/dla_aot_splitter/dla_aot_splitter \
  -cm <full_path_to_compiled_model_file> \
  -i < XXX-delimited list of inference input files> \
  -plugins <full_path_to_plugins_aot_splitter.xml> \
  [-bgr]

    The optional -bgr command option tells the AOT splitter to reverse the R, G, and B input channels for images before the channels are passed to the model.

    For example: 
    runtime/build_Release/dla_aot_splitter/dla_aot_splitter \
  -cm $COREDLA_ROOT/demo/RN50_Performance_b1.bin \
  -i $COREDLA_ROOT/demo/sample_images/val_00000000.bmp \
  -plugins \
   runtime/dla_aot_splitter/dla_aot_splitter_plugin/plugins_aot_splitter.xml
  -bgr
Running the splitter generates the following files:
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