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Add information about Vivado for part 7 #90

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Dec 12, 2024
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Add information about Vivado for part 7 #90

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35969c4
add information about Vivado for part 7
JanFSchulte Dec 12, 2024
aad54cd
Merge branch 'main' into vitis
JanFSchulte Dec 12, 2024
15b60e2
add a mention of Vivdao to the part7a notebook
JanFSchulte Dec 12, 2024
d23d40a
fix typo
JanFSchulte Dec 12, 2024
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2 changes: 2 additions & 0 deletions README.md
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Expand Up @@ -27,6 +27,8 @@ conda activate hls4ml-tutorial
source /path/to/your/installtion/Xilinx/Vitis_HLS/202X.X/settings64.(c)sh
```

Note that part 7 of the tutorial makes use of the `VivadoAccelator` backend of hls4ml for which no Vitis equivalent is available yet. For this part of the tutorial it is therefore necesary to install and source Vivado HLS version 2019.2 or 2020.1, which can be obtained [here](https://www.xilinx.com/support/download/index.html/content/xilinx/en/downloadNav/vivado-design-tools/archive.html).

## Companion material
We have prepared a set of slides with some introduction and more details on each of the exercises.
Please find them [here](https://docs.google.com/presentation/d/1c4LvEc6yMByx2HJs8zUP5oxLtY6ACSizQdKvw5cg5Ck/edit?usp=sharing).
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6 changes: 3 additions & 3 deletions part7a_bitstream.ipynb
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Expand Up @@ -7,7 +7,7 @@
"source": [
"# Part 7a: Bitstream Generation\n",
"\n",
"In the previous sections we've seen how to train a Neural Network with a small resource footprint using QKeras, then to convert it to `hls4ml` and create an IP. That IP can be interfaced into a larger design to deploy on an FPGA device. In this section, we introduce the `VivadoAccelerator` backend of `hls4ml`, where we can easily target some supported devices to get up and running quickly. Specifically, we'll deploy the model on a [pynq-z2 board](http://www.pynq.io/)."
"In the previous sections we've seen how to train a Neural Network with a small resource footprint using QKeras, then to convert it to `hls4ml` and create an IP. That IP can be interfaced into a larger design to deploy on an FPGA device. In this section, we introduce the `VivadoAccelerator` backend of `hls4ml`, where we can easily target some supported devices to get up and running quickly. Specifically, we'll deploy the model on a [pynq-z2 board](http://www.pynq.io/). NOTE: This tutorial reuires on Vivado HLS instead of Vitis."
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]
},
{
Expand All @@ -26,7 +26,7 @@
"_add_supported_quantized_objects(co)\n",
"import os\n",
"\n",
"os.environ['PATH'] = os.environ['XILINX_Vivado'] + '/bin:' + os.environ['PATH']"
"os.environ['PATH'] = os.environ['XILINX_VIVADO'] + '/bin:' + os.environ['PATH']"
]
},
{
Expand Down Expand Up @@ -74,7 +74,7 @@
"import hls4ml\n",
"import plotting\n",
"\n",
"config = hls4ml.utils.config_from_keras_model(model, granularity='name', backend='Vitis')\n",
"config = hls4ml.utils.config_from_keras_model(model, granularity='name')\n",
"config['LayerName']['softmax']['exp_table_t'] = 'ap_fixed<18,8>'\n",
"config['LayerName']['softmax']['inv_table_t'] = 'ap_fixed<18,4>'\n",
"for layer in ['fc1', 'fc2', 'fc3', 'output']:\n",
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