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

2.5.1. Architecture Description File Format for Instance Parameterization

The FPGA AI Suite IP has a highly configurable architecture. Configuring the design allows for different trade-offs between inference performance (throughput and latency) and utilization of FPGA resources (area). Configurations are specified through Architecture Description Files. The IP instances corresponding to these configurations can be compiled as part of an FPGA design into an FPGA bitstream.

The architecture determines how much FPGA area is consumed by the FPGA AI Suite IP and strongly affects the achieved inference fps and ease of timing closure.

Achieving the best performance of a given graph for a given FPGA area (or the smallest FPGA area for a given performance target) requires optimizing the architecture. The architecture optimization function of the FPGA AI Suite compiler is designed to produce good architectures for a given graph or set of graphs. For more details about the architecture optimization function of the compiler, refer to the FPGA AI Suite Compiler Reference Manual .

The FPGA AI Suite Architecture Description Files use the protobuf format and have a .arch file extension. While these files are human readable and editable, manually optimizing an architecture requires a deep knowledge of the FPGA AI Suite IP design and is not recommended.

You adjust some of the architecture parameters by hand, because the Architecture Optimizer does not modify them. For example, the optimizer does not modify the numerical precision (for example, fp16 or fp11) in the architecture file. Similarly, the optimizer does not modify details related to the AXI interfaces on the IP. In some case, you can improve performance of the resulting optimized architecture by the choice of these values.

When possible, modifying the graph or batch size might also result in performance improvements. For example, a graph that requires FP16 precision might have sufficient accuracy at FP11 or FP12 if a few extra layers are added. Reducing the internal precision enables a large memory and area reduction. Very small and fast graphs might achieve a higher performance on hardware by using a batch size that is greater than one.

The example_architectures/ directory includes an example that shows how to enable the hardware-accelerated softmax function.

The comment character in the .arch format is #.

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