Tutorial

This repository provides a tutorial on using hls4ml for training and deploying machine learning models on Alveo boards (U50, U200, U250 and U280).

Software Requirements

Vivado and Vitis 2020.1
Platform files for your board (2020.1 or older)
XRT (2020.1 or newer)

Install Drivers

The XRT and platform files can be found at the below link with installation guidlines in Chapter 2.
Installation.

Install PYNQ package

pip install pynq

Install hls4ml

Run the below command to install the correct branch along with the dependencies:

pip install git+https://github.com/selwyn96/hls4ml.git@alveo_dev#egg=hls4ml[profiling]

Neural network IP and binary generation

Following the notebook the IP can be generated (with axi stream interface) and the .xclbin file can be generated here (this should take ~1h).

Code

The files are organized as follows

• training : contains the notebook used to create the Keras model and generate the xclbin container.
• Drivers : Xilinx Runtime and Platform files can be moved and installed from this directory.
• Deployment : contains the notebook used for deploying the model on the board.

For the make_xclbin fuction, specify the the directory where the platform files are installed. By default it should be installed at /opt/xilinx/platforms.

The xclbin file will be generated in the xclbin_files folder under your project folder.

General results

The tests are performed on a Alveo u50 board.

Vivado HLS resource utilization [MNIST classification]

Model	BRAM[Blocks]	DSP	FF[k]	LUT[k]
Platform	/	/	/	/
Quntized+Pruned CNN	365	10	68.8	129.0
Quntized+Pruned Dense	418	10	51.3	188.4

Post-implementation resource utilization [MNIST classification]

Model	BRAM[Blocks]	DSP	FF[k]	LUT[k]
Platform	178	4	123.4	100.2
Quntized+Pruned CNN	42	10	28.7	22.6
Quntized+Pruned Dense	92	10	54.4	38.7

NN performance

MODEL	Accuracy [%]	Rate[Images\s]	timg[μs]
CNN	97.11	52600	19
DNN	91.31	125000	8

Comparisons

Here are presented the prediction times for different devices (10000 samples dataset).

Device	timgCNN[μs]	timgDNN[μs]
CPU[3700X]	95	24
GPU[RX 580]	30	22
ZYNQ[PYNQ_Z2]	87	85
ALVEO[u50]	19	8