Configuration Options

The DPU can be configured with some predefined options, which includes the number of DPU cores, the convolution architecture, DSP cascade, DSP usage, and UltraRAM usage. These options allow you to set the DSP slice, LUT, block RAM, and UltraRAM usage. The following figure shows the configuration page of the DPU.

Figure 1: DPU Configuration – Arch Tab
Number of DPU Cores
A maximum of four cores can be selected in one DPU IP. Multiple DPU cores can be used to achieve higher performance. Consequently, it consumes more programmable logic resources.

Contact your local Xilinx sales representative if more than four cores are required.

Arch of DPU
The DPU IP can be configured with various convolution architectures which are related to the parallelism of the convolution unit. The architectures for the DPU IP include B512, B800, B1024, B1152, B1600, B2304, B3136, and B4096.

There are three dimensions of parallelism in the DPU convolution architecture: pixel parallelism, input channel parallelism, and output channel parallelism. The input channel parallelism is always equal to the output channel parallelism (this is equivalent to channel_parallel in the previous table). The different architectures require different programmable logic resources. The larger architectures can achieve higher performance with more resources. The parallelism for the different architectures is listed in the following table.

Table 1. Parallelism for Different Convolution Architectures
Convolution Architecture Pixel Parallelism (PP) Input Channel Parallelism (ICP) Output Channel Parallelism (OCP) Peak Ops (operations/per clock)
B512 4 8 8 512
B800 4 10 10 800
B1024 8 8 8 1024
B1152 4 12 12 1150
B1600 8 10 10 1600
B2304 8 12 12 2304
B3136 8 14 14 3136
B4096 8 16 16 4096
  1. In each clock cycle, the convolution array performs a multiplication and an accumulation, which are counted as two operations. Thus, the peak number of operations per cycle is equal to PP*ICP*OCP*2.
RAM Usage
The weights, bias, and intermediate features are buffered in the on-chip memory. The on-chip memory consists of RAM which can be instantiated as block RAM and UltraRAM. The RAM Usage option determines the total amount of on-chip memory used in different DPU architectures, and the setting is for all the DPU cores in the DPU IP. High RAM Usage means that the on-chip memory block will be larger, allowing the DPU more flexibility in handling the intermediate data. High RAM Usage implies higher performance in each DPU core. The number of BRAM36K blocks used in different architectures for low and high RAM Usage is illustrated in the following table.
Note: The DPU instruction set for different options of RAM Usage is different. When the RAM Usage option is modified, the DPU instructions file should be regenerated by recompiling the neural network. The following results are based on a DPU with depthwise convolution.
Table 2. Number of BRAM36K Blocks in Different Architectures for Each DPU Core
DPU Architecture Low RAM Usage High RAM Usage
B512 (4x8x8) 73.5 89.5
B800 (4x10x10) 91.5 109.5
B1024 (8x8x8) 105.5 137.5
B1152 (4x12x12) 123 145
B1600 (8x10x10) 127.5 163.5
B2304 (8x12x12) 167 211
B3136 (8x14x14) 210 262
B4096 (8x16x16) 257 317.5
Channel Augmentation
Channel augmentation is an optional feature for improving the efficiency of the DPU when the number of input channels is much lower than the available channel parallelism. For example, the input channel of the first layer in most CNNs is three, which does not fully utilize all the available hardware channels. However, when the number of input channels is larger than the channel parallelism, then enabling channel augmentation.

Thus, channel augmentation can improve the total efficiency for most CNNs, but it will cost extra logic resources. The following table illustrates the extra LUT resources used with channel augmentation and the statistics are for reference.

Table 3. Extra LUTs of DPU with Channel Augmentation
DPU Architecture Extra LUTs with Channel Augmentation
B512(4x8x8) 3121
B800(4x10x10) 2624
B1024(8x8x8) 3133
B1152(4x12x12) 1744
B1600(8x10x10) 2476
B2304(8x12x12) 1710
B3136(8x14x14) 1946
B4096(8x16x16) 1701
In standard convolution, each input channel needs to perform the operation with one specific kernel, and then the result is obtained by combining the results of all channels together.

In depthwise separable convolution, the operation is performed in two steps: depthwise convolution and pointwise convolution. Depthwise convolution is performed for each feature map separately as shown on the left side of the following figure. The next step is to perform pointwise convolution, which is the same as standard convolution with kernel size 1x1. The parallelism of depthwise convolution is half that of the pixel parallelism.

Figure 2: Depthwise Convolution and Pointwise Convolution
Table 4. Extra resources of DPU with Depthwise Convolution
DPU Architecture Extra LUTs Extra BRAMs Extra DSPs
B512(4x12x12) 1734 4 12
B800(4x10x10) 2293 4.5 15
B1024(8x8x8) 2744 4 24
B1152(4x12x12) 2365 5.5 18
B1600(8x10x10) 3392 4.5 30
B2304(8x12x12) 3943 5.5 36
B3136(8x14x14) 4269 6.5 42
B4096(8x16x16) 4930 7.5 48
The AveragePool option determines whether the average pooling operation will be performed on the DPU or not. The supported sizes range from 2x2, 3x3, …, to 8x8, with only square sizes supported.
The extra resources with Average Pool is listed in the following table.
Table 5. Extra LUTs of DPU with Average Pool
DPU Architecture Extra LUTs
B512(4x12x12) 1507
B800(4x10x10) 2016
B1024(8x8x8) 1564
B1152(4x12x12) 2352
B1600(8x10x10) 1862
B2304(8x12x12) 2338
B3136(8x14x14) 2574
B4096(8x16x16) 3081
ReLU Type
The ReLU Type option determines which kind of ReLU function can be used in the DPU. ReLU and ReLU6 are supported by default.
The option “ReLU + LeakyReLU + ReLU6“ means that LeakyReLU becomes available as an activation function.
Note: LeakyRelU coefficient is fixed to 0.1.
Table 6. Extra LUTs with ReLU + LeakyReLU + ReLU6 compared to ReLU+ReLU6
DPU Architecture Extra LUTs
B512(4x12x12) 347
B800(4x10x10) 725
B1024(8x8x8) 451
B1152(4x12x12) 780
B1600(8x10x10) 467
B2304(8x12x12) 706
B3136(8x14x14) 831
B4096(8x16x16) 925
This option allows the softmax function to be implemented in hardware. The hardware implementation of softmax can be 160 times faster than a software implementation. Enabling this option depends on the available hardware resources and desired throughput.

When softmax is enabled, an AXI master interface named SFM_M_AXI and an interrupt port named sfm_interrupt will appear in the DPU IP. The softmax module uses m_axi_dpu_aclk as the AXI clock for SFM_M_AXI as well as for computation. The softmax function is not supported in Zynq®-7000 devices.

The extra resources with Softmax enabled are listed in the following table.

Table 7. Extra LUTs with Softmax
IP Name Extra LUTs Extra FFs Extra BRAMsE Extra DSPs
Softmax 9580 8019 4 14