Intel® FPGA AI Suite: IP Reference Manual

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ID 768974
Date 9/06/2023
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Document Table of Contents
Document Table of Contents x
1. Intel® FPGA AI Suite IP Reference Manual 2. About the Intel® FPGA AI Suite IP 3. Intel® FPGA AI Suite IP Generation Utility 4. Intel® FPGA AI Suite Ahead-of-Time Splitter Utility 5. CSR Map and Descriptor Queue A. Intel® FPGA AI Suite IP Reference Manual Archives B. Intel® FPGA AI Suite IP Reference Manual Document Revision History
2. About the Intel® FPGA AI Suite IP x
2.1. Supported Models 2.2. Model Performance 2.3. Intel® FPGA AI Suite Layer / Primitive Ranges 2.4. Intel® FPGA AI Suite IP Block Configuration 2.5. IP Block Interfaces
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)
2.4. Intel® FPGA AI Suite IP Block Configuration x
2.4.1. Architecture Description File Format for Instance Parameterization 2.4.2. Architecture Description File Parameters
2.4.2. Architecture Description File Parameters x
2.4.2.1. Parameter group: Global Parameters 2.4.2.2. Parameter group: activation 2.4.2.3. Parameter group: pe_array 2.4.2.4. Parameter group: pool 2.4.2.5. Module: softmax 2.4.2.6. Parameter group: dma 2.4.2.7. Parameter group: xbar 2.4.2.8. Parameter group: filter_scratchpad 2.4.2.9. Parameter group: config_network
2.5. IP Block Interfaces x
2.5.1. Clock and Reset 2.5.2. AXI Interfaces 2.5.3. AXI Interface Clock and Reset 2.5.4. Input Feature Tensor In-Memory Format 2.5.5. Output Tensor In-Memory Format
2.5.4. Input Feature Tensor In-Memory Format x
2.5.4.1. Multiple Input Graphs 2.5.4.2. Input Folding 2.5.4.3. Input Scale and Shift 2.5.4.4. Input Transform Mapping
3. Intel® 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. Intel® FPGA AI Suite Ahead-of-Time Splitter Utility x
4.1. Files Generated by the Intel® FPGA AI Suite Ahead-of-Time (AOT) Splitter Utility 4.2. Building the Intel® FPGA AI Suite Ahead-of-Time (AOT) Splitter Utility 4.3. Running the Intel® FPGA AI Suite Ahead-of-Time (AOT) Splitter Utility 4.4. Intel® 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
1. Intel® FPGA AI Suite IP Reference Manual
2. About the Intel® FPGA AI Suite IP
3. Intel® FPGA AI Suite IP Generation Utility
4. Intel® FPGA AI Suite Ahead-of-Time Splitter Utility
5. CSR Map and Descriptor Queue
A. Intel® FPGA AI Suite IP Reference Manual Archives
B. Intel® FPGA AI Suite IP Reference Manual Document Revision History

2.3. Intel® FPGA AI Suite Layer / Primitive Ranges

The following table lists the hyperparameter ranges supported by key primitive layers:

Layer / Primitive

Hyperparameter

Supported Range

Fully connected

None

n/a

2D Conv

Filter Size

Width = [1..28]

Height = [1..28]

Height does not have to equal width.

Default value for each is 14.

Stride

Maximum stride is 15

Pad

Maximum pad is (216) - 1

3D Conv Filter Size Width = [1..28]

Height = [1..28]

Depth = [1..14]

Filter volume should fit into the filter cache size.

Stride Maximum stride is 15.
Pad Maximum pad is (216) - 1

Depthwise

Filter Size

Same as 2D Conv filter size

Depth = 1

Stride

Same as 2D Conv stride

Depth = 1

Pad

Same as 2D conv padding

Depth = 1

Scale-Shift

Scale factor

FP16 float range

Bias term

FP16 float range

Deconv / Transpose Convolution

Filter Size

Any – Same as convolution, and height/width can be different

Depth = 1

Stride

1, 2, 4, 8 (stride width == stride height)

Depth = 1

Pad

Restricted to filter_[height, width] - 1

Depth = 1

ReLU

n/a

n/a

pReLU

Scaling parameter (a) (1 per filter / conv output channel)

float range

Depth = 1

Leaky ReLU

Scaling parameter (a) (1 per tensor)

float range

Clamp

Limit parameters (a, b) (1 per tensor)

float range
Round_Clamp Limit parameters (a, b) (1 per tensor) float range

H-sigmoid

n/a

n/a

H-swish

n/a

n/a

Max Pool

Window Size

3×3×3, 4×4×4, 5×5×5, 6×6×6, 7×7×7

Pad

1, 2

Stride

1, 2, 3, 4

Average Pool

Window Size

Up to 27x27 (one less than the maximum 2D convolution size)

Width == Height

Depth = 1 or 2

Pad

1, 2

Stride

1, 2, 3, 4

Softmax

n/a

n/a

Elementwise Multiplication of feature * filter and feature * feature tensors.2

n/a

Tensor sizes are expanded if necessary to support the multiplication.

Depth = 1
ChannelToSpace

DepthToSpace

PixelShuffle

 block_mode blocks_first or blocks_last
block_size 2, 4, 8
2 This is an element-wise multiplication, not a matrix multiply operation.
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