File size: 17,789 Bytes
8ddd53d e3f6d55 8ddd53d e3f6d55 8ddd53d e3f6d55 8ddd53d 78a19bd 8ddd53d 78a19bd 8ddd53d 78a19bd 8ddd53d 78a19bd 8ddd53d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 |
---
license: cc-by-nc-4.0
task_categories:
- text-generation
- feature-extraction
- question-answering
- summarization
- tabular-regression
- tabular-classification
language:
- en
tags:
- FPGA
- hls4ml
- HLS
- VHDL
- GNN
- Transformer
- Benchmark
- Latency
- Resource
- Estimation
- Regression
pretty_name: wa-hls4ml Projects
size_categories:
- 100K<n<1M
configs:
- config_name: default
data_files:
# - split: rule4ml
# path:
# - 2_20.csv
# - 2_20/*.tar.gz
# - split: 2layer
# path:
# - 2layer.csv
# - 2layer/*.tar.gz
# - split: 3layer
# path:
# - 3layer.csv
# - 3layer/*.tar.gzn
- split: conv1d
path:
- conv1d.csv
- conv1d/*.tar.gz
# - split: conv2d
# path:
# - conv2d.csv
# - conv2d/*.tar.gz
# - split: latency
# path:
# - latency.csv
# - latency/*.tar.gz
# - split: resource
# path:
# - resource.csv
# - resource/*.tar.gz
# - split: exemplar
# path:
# - exemplar.csv
# - exemplar/*.tar.gz
---
# Dataset Card for wa-hls4ml Benchmark Dataset
The wa-hls4ml projects dataset, comprized of the Vivado/Vitis projects of neural networks converted into HLS Code via hls4ml. Projects are complete, and include all logs, HLS Code, VHDL Code, Intermediete Representations, and source keras models.
This is a companion dataset to the [wa-hls4ml](https://huggingface.co/datasets/fastmachinelearning/wa-hls4ml) dataset. There is a reference CSV for each model type that contains a reference to each individual model name, the artifacts file for that model, and which batch archive file contains that project archive for that model.
**PLEASE NOTE:** The dataset is currently incomplete, and is in the process of being cleaned and uploaded.
## Dataset Details
We introduce **wa-hls4ml**[^1]: a **dataset** unprecedented in scale and features and a **benchmark** for common evaluation
The open **dataset** is unprecedented in terms of its size, with over 680\,000 fully synthesized dataflow models.
The goal is to continue to grow and extend the dataset over time.
We include all steps of the synthesis chain from ML model to HLS representation to register-transfer level (RTL) and save the full logs.
This will enable a much broader set of applications beyond those in this paper.
The **benchmark** standardizes evaluation of the performance of resource usage and latency estimators across a suite of metrics, such as the coefficient of determination (R^2), symmetric mean absolute percentage error (SMAPE), and root mean square error (RMSE), and provides sample models, both synthetic and from scientific applications, to support and encourage the continued development of better surrogate models.
[^1]: Named after Wario and Waluigi who are doppelgängers of Mario and Luigi, respectively, in the Nintendo Super Mario platform game series.
### Dataset Description
The dataset has two primary components, each designed to test different aspects of a surrogate model's performance.
The first part is based on *synthetic* neural networks generated with various layer types, micro-architectures, and precisions.
This synthetic dataset lets us systematically explore the FPGA resources and latencies as we vary different model parameters.
The second part of the benchmark targets models from *exemplar realistic* scientific applications, requiring real-time processing at the edge, near the data sources.
Models with real-time constraints constitute a primary use case for ML-to-FPGA pipelines like hls4ml.
This part tests the ability of the surrogate model to extrapolate its predictions to new configurations and architectures beyond the training set, assessing the model's robustness and performance for real applications.
#### Exemplar Model Descriptions
- **Jet**: A fully connected neural network that classifies simulated particle jets originating from one of five particle classes in high-energy physics experiments.
- **Top Quarks**: A binary classifier for top quark jets, helping probe fundamental particles and their interactions.
- **Anomaly**: An autoencoder trained on audio data to reproduce the input spectrogram, whose loss value differentiates between normal and abnormal signals.
- **BiPC**: An encoder that transforms high-resolution images, producing sparse codes for further compression.
- **CookieBox**: Dedicated to real-time data acquisition for the CookieBox system, designed for advanced experimental setups requiring rapid handling of large data volumes generated by high-speed detectors.
- **AutoMLP**: A fully connected network from the AutoMLP framework, focusing on accelerating MLPs on FPGAs, providing significant improvements in computational performance and energy efficiency.
- **Particle Tracking**: Tracks charged particles in real-time as they traverse silicon detectors in large-scale particle physics experiments.
#### Exemplar Model Architectures
| **Model** | **Size** | **Input** | **Architecture** |
|--------------------------|----------|-----------|----------------------------------------------------------------------------------|
| **Jet** | 2,821 | 16 | →[ReLU]32 →[ReLU]32 →[ReLU]32 →[Softmax]5 |
| **Top Quarks** | 385 | 10 | →[ReLU]32 →[Sigmoid]1 |
| **Anomaly** | 2,864 | 128 | →[ReLU]8 →[ReLU]4 →[ReLU]128 →[ReLU]4 →[Softmax]128 |
| **BiPC** | 7,776 | 36 | →[ReLU]36 →[ReLU]36 →[ReLU]36 →[ReLU]36 →[ReLU]36 |
| **CookieBox** | 3,433 | 512 | →[ReLU]4 →[ReLU]32 →[ReLU]32 →[Softmax]5 |
| **AutoMLP** | 534 | 7 | →[ReLU]12 →[ReLU]16 →[ReLU]12 →[Softmax]2 |
| **Particle Tracking** | 2,691 | 14 | →[ReLU]32 →[ReLU]32 →[ReLU]32 →[Softmax]3 |
- **Curated by:** Fast Machine Learning Lab
- **Funded by:** See "Acknowledgements" in the paper for full funding details
- **Language(s) (NLP):** English
- **License:** cc-by-nc-4.0
### Dataset Sources
The Dataset was consists of data generated by the authors using the following methods:
#### Generation of Synthetic Data
The train, validation, and test sets were created by first generating models of varying architectures in the Keras and QKeras Python libraries, varying their hyperparameters.
The updated rule4ml dataset follows the same generation method and hyperparameter ranges described in prior work, while adding II information and logic synthesis results to the reports.
For the remaining subsets of the data, the two-layer and three-layer fully-connected models were generated using a grid search method according to the parameter ranges mentioned below, whereas larger fully-connected models and convolutional models (one- and two-dimensional) were randomly generated, where convolutional models also contain dense, flatten, and pooling layers.
The weight and bias precision was implemented in HLS as datatype `ap_fixed<X,1>`, where `X` is the specified precision and the total number of bits allocated to the weight and bias values, with one bit being reserved for the integer portion of the value.
These models were then converted to HLS using hls4ml and synthesized through AMD Vitis version 2023.2 and 2024.2, targeting the AMD Xilinx Alveo U250 FPGA board.
The model sets have the following parameter ranges:
- **Number of layers**: 2–7 for fully-connected models; 3–7 for convolutional models.
- **Activation functions**: Linear for most 2–3 layer fully-connected models; ReLU, tanh, and sigmoid for all other fully-connected models and convolutional models.
- **Number of features/neurons**: 8–128 (step size: 8 for 2–3 layer) for fully-connected models; 32–128 for convolution models with 8–64 filters.
- **Weight and bias bit precision**: 2–16 bits (step size: 2) for 2–3 layer fully-connected models, 4–16 bits (step size: powers of 2) for 3–7 layer fully-connected and convolutional models.
- **hls4ml target reuse factor**: 1–4093 for fully-connected models; 8192–32795 for convolutional models.
- **hls4ml implementation strategy**: Resource strategy, which controls the degree of parallelism by explicitly specifying the number of MAC operations performed in parallel per clock cycle, is used for most fully-connected models and all convolutional models, while Latency strategy, where the computation is unrolled, is used for some 3–7 layer fully-connected models.
- **hls4ml I/O type**: The io_parallel setting, which directly wires the output of one layer to the input of the next layer, is used for all fully-connected models, and the io_stream setting, which uses FIFO buffers between layers, is used for all convolutional models.
#### Exemplar Model Synthesis Parameters
The exemplar models were synthesized with the following parameters:
| **Hyperparameter** | **Values** |
|---------------------------|---------------------------------------------------------------------------|
| **Precision** | `ap_fixed<2,1>`, `ap_fixed<8,3>`, `ap_fixed<16,6>` |
| **Strategy** | `Latency`, `Resource` |
| **Target reuse factor** | 1, 128, 1024 |
| **Target board** | Alveo U200, Alveo U250 |
| **Target clock** | 5 ns, 10 ns |
| **Vivado version** | 2019.1, 2020.1 |
The synthesis was repeated multiple times, varying the hls4ml *reuse factor*, a tunable setting that proportionally limits the number of multiplication operations used.
The hls4ml conversion, HLS synthesis, and logic synthesis of the train and test sets were all performed in parallel on the National Research Platform Kubernetes Hypercluster and the Texas A&M ACES HPRC Cluster.
On the National Research Platform, synthesis was run inside a container with a guest OS of Ubuntu 20.04.4 LTS, the containers being slightly modified versions of the xilinx-docker v2023.2 "user" images, with 3 virtual CPU cores and 16 GB of RAM per pod, with all AMD tools mounted through a Ceph-based persistent volume.
Jobs run on the Texas A&M ACES HPRC Cluster were run using Vitis 2024.2, each with 2 virtual CPU cores and 32 GB of RAM.
The resulting projects, reports, logs, and a JSON file containing the resource/latency usage and estimates of the C and logic synthesis were collected for each sample in the dataset.
The data, excluding the projects and logs, were then further processed into a collection of JSON files, distributed alongside this paper and described below.
- **Repository:** [fastmachinelearning/wa-hls4ml-paper](https://github.com/fastmachinelearning/wa-hls4ml-paper)
- **Paper [optional]:** In Review
---
## Uses
This dataset is intended to be used to train/refine LLMs to better generate HLS and VHDL code, along with improving the understanding of the general C- and Logic-Synthesis processes to better assist in debugging and question answering for FPGA tooling and hls4ml.
### Direct Use
This dataset is generated using the tool hls4ml, and should be used to train LLMs and/or other models for HLS/VHDL Code generation along with improving qustion answering and understanding of (currently) Vivado/Vitis and hls4ml workflows.
### Out-of-Scope Use
As this dataset is generated using the hls4ml and Vivado/Vitis tools, it should not be used to train LLMs and/or other models for other tools, as results and implementation details may vary across those tools compared to hls4ml and Vivado/Vitis.
---
## Dataset Structure
Within each subset, excluding the exemplar test set, the data is grouped as follows.
- **2_20 (rule4ml)**: The updated rule4ml dataset, containing fully-connected neural networks that were randomly generated with layer counts between 2 and 20 layers, using hls4ml resource and latency strategies.
- **2_layer**: A subset containing 2-layer deep fully-connected neural networks generated via a grid search using hls4ml resource and io_parallel strategies.
- **3_layer**: A subset containing 3-layer deep fully-connected neural networks generated via a grid search using hls4ml resource and io_parallel strategies.
- **conv1d**: A subset containing 3–7 layer deep 1-dimensional convolutional neural networks that were randomly generated and use hls4ml resource and io_stream strategies.
- **conv2d**: A subset containing 3–7 layer deep 2-dimensional convolutional neural networks that were randomly generated and use hls4ml resource and io_stream strategies.
- **latency**: A subset containing 3–7 layer deep fully-connected neural networks that were randomly generated and use hls4ml latency and io_parallel strategies.
- **resource**: A subset containing 3–7 layer deep fully-connected neural networks that were randomly generated and use hls4ml resource and io_parallel strategies.
### Structure of the CSV Index files
There is one CSV index file for each model type split. Each file has 3 fields:
- **Model Name**: The name of the model that you can use to reference the corresponding JSON file in the [wa-hls4ml](fastmachinelearning/wa-hls4ml) dataset. **Note:** you will need to split the string at the last `_` character to find the corresponding JSON file. The string to the left of the `_` is the source model name, and the string to the right is the target reuse factor for that specific project.
- **Artifacts File**: The name of the specific artifacts file that contains the project for the specified model.
- **Archive Name**: The name of the archive that contains the specific artifacts file for the specified model.
### Structure of the project archives
Due to file size/count limitations, the individual project archives are split into batches and placed into one of a number of tar.gz files. If you are looking to find a specific project file, please refer to the Index CSV file as mentioned above.
Each project archive contains the complete Vivado/Vitis project in it's original structure, including resulting HLS and VHDL Code, logs, reports, intermediete representations, and source keras model file.
---
## Curation Rationale
With the introduction of ML into FPGA toolchains, e.g., for resource and latency prediction or code generation, there is a significant need for large datasets to support and train these tools.
We found that existing datasets were insufficient for these needs, and therefore sought to build a dataset and a highly scalable data generation framework that is useful for a wide variety of research surrounding ML on FPGAs.
This dataset serves as one of the few openly accessible, large-scale collections of synthesized neural networks available for ML research.
### Exemplar Realistic Models
The exemplar models utilized in this study include several key architectures, each tailored for specific ML tasks and targeting scientific applications with low-latency constraints.
### Source Data
The data was generated via randomly generated neural networks and specifically selected exemplar models, converted into HLS Code via hls4ml, with the resulting latency values collected after performing C-Synthesis through Vivado/Vitis HLS on the resulting HLS Code, and resource values collected after performing logic synthesis through Vivado/Vitis on the resulting HDL Code. The projects were then stored in a tar.gz file and distributed in this dataset.
### Who are the source data producers?
[Benjamin Hawks](https://orcid.org/0000-0001-5700-0288), Fermi National Accelerator Laboratory, USA
[Hamza Ezzaoui Rahali](https://orcid.org/0000-0002-0352-725X), University of Sherbrooke, Canada
[Mohammad Mehdi Rahimifar](https://orcid.org/0000-0002-6582-8322), University of Sherbrooke, Canada
### Personal and Sensitive Information
This data contains no personally identifiable or sensitive information except for the names/usernames of the authors in some file paths.
## Bias, Risks, and Limitations
In it's inital form, a majority of this dataset is comprised of very small (2-3 layer) dense neural networks without activations. This should be considered when training a model on it, and appropriate measures should be taken to weight the data at training time.
We intend to continuously update this dataset, addressing this imbalance over time as more data is generated.
### Recommendations
Appropriate measures should be taken to weight the data to account for the dataset imbalance at training time.
## Citation
Paper currently in review.
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Dataset Card Authors
[Benjamin Hawks](https://orcid.org/0000-0001-5700-0288), Fermi National Accelerator Laboratory, USA
[Hamza Ezzaoui Rahali](https://orcid.org/0000-0002-0352-725X), University of Sherbrooke, Canada
[Mohammad Mehdi Rahimifar](https://orcid.org/0000-0002-6582-8322), University of Sherbrooke, Canada
## Dataset Card Contact
[[email protected]](mailto:[email protected]) |