rule4ml 0.2.0

Resource utilization and Latency Estimation for ML on FPGA

rule4ml: Resource Utilization and Latency Estimation for ML

rule4ml is a tool designed for pre-synthesis estimation of FPGA resource utilization and inference latency for machine learning models.

Installation

rule4ml releases are uploaded to the Python Package Index for easy installation via pip.

pip install rule4ml

This will only install the base package and its dependencies for resources and latency prediction. The data_gen scripts and the Jupyter notebooks are to be cloned from the repo if needed.

The data generation dependencies are listed seperately in data_gen/requirements.txt, or can be installed with:

pip install rule4ml[datagen]

Getting Started

Tutorial

To get started with rule4ml, please refer to the detailed Jupyter Notebook tutorial. This tutorial covers:

• Using pre-trained estimators for resources and latency predictions.
• Generating synthetic datasets.
• Training and testing your own predictors.

Usage

Here's a quick example of how to use rule4ml to estimate resources and latency for a given model:

import keras
from keras.layers import Input, Dense, Activation

from rule4ml.models.wrappers import MultiModelWrapper

# Example of a simple keras Model
input_size = 16
inputs = Input(shape=(input_size,))
x = Dense(32, activation="relu")(inputs)
x = Dense(32, activation="relu")(x)
x = Dense(32, activation="relu")(x)
outputs = Dense(5, activation="softmax")(x)

model_to_predict = keras.Model(inputs=inputs, outputs=outputs, name="Jet Classifier")
model_to_predict.build((None, input_size))  # building keras models is required

# Loading default predictors
estimator = MultiModelWrapper()
estimator.load_default_models()

# MultiModelWrapper predictions are formatted as a pandas DataFrame
prediction_df = estimator.predict(model_to_predict)

# Further formatting can be applied to organize the DataFrame
if not prediction_df.empty:
    prediction_df = prediction_df.groupby(
        ["Model", "Board", "Strategy", "Precision", "Reuse Factor", "HLS4ML Version", "Vivado Version"], observed=True
    ).mean()  # each row is unique in the groupby, mean() is only called to convert DataFrameGroupBy

# Outside of Jupyter notebooks, we recommend saving the DataFrame as HTML for better readability
prediction_df.to_html("keras_example.html")

keras_example.html (truncated)

							BRAM	BRAM (%)	DSP	DSP (%)	FF	FF (%)	LUT	LUT (%)	CYCLES	INTERVAL
Model	Board	Strategy	Precision	Reuse Factor	HLS4ML Version	Vivado Version
Jet Classifier	pynq-z2	latency	ap_fixed<2, 1>	1	0.8.1	2019.1	2.52	0.90	0.32	0.14	1265.02	1.19	3564.90	6.70	125.77	1.35
2019.2	2.47	0.88	0.48	0.22	1262.29	1.19	3380.57	6.35	115.48	1.35
2020.1	2.29	0.82	0.49	0.22	1109.34	1.04	3279.37	6.16	115.62	1.35
2020.2	2.55	0.91	0.53	0.24	1490.04	1.40	3457.23	6.50	118.07	1.35
2021.1	2.31	0.83	0.44	0.20	1054.50	0.99	2915.67	5.48	118.99	1.35
2021.2	2.48	0.89	0.58	0.26	1085.17	1.02	3072.19	5.77	117.91	1.35
2022.1	2.53	0.90	0.47	0.21	1301.50	1.22	3093.67	5.82	119.36	1.35
2022.2	2.43	0.87	0.57	0.26	1150.09	1.08	3032.74	5.70	119.39	1.35
2023.1	2.51	0.90	0.59	0.27	1357.55	1.28	3327.19	6.25	118.30	1.35
2023.2	2.39	0.85	0.29	0.13	304.04	0.29	2689.27	5.06	108.34	1.35
2024.1	2.41	0.86	0.54	0.25	1574.28	1.48	3517.61	6.61	116.26	1.35
2024.2	2.08	0.74	0.77	0.35	936.16	0.88	2780.73	5.23	110.77	1.35
1.1.0	2019.1	2.57	0.92	1.16	0.53	1237.20	1.16	2434.88	4.58	37.70	1.35
2019.2	2.53	0.90	1.39	0.63	1273.41	1.20	2317.88	4.36	28.73	1.35
2020.1	2.35	0.84	1.42	0.65	1023.07	0.96	2275.59	4.28	28.97	1.35
2020.2	2.64	0.94	1.45	0.66	1314.61	1.24	2359.94	4.44	30.62	1.35
2021.1	2.34	0.84	1.35	0.61	983.35	0.92	2025.47	3.81	31.37	1.35
2021.2	2.56	0.91	1.50	0.68	1149.12	1.08	2167.54	4.07	30.66	1.35
2022.1	2.65	0.95	1.39	0.63	1104.21	1.04	2131.50	4.01	31.74	1.35
2022.2	2.47	0.88	1.49	0.68	1200.66	1.13	2120.53	3.99	31.79	1.35
2023.1	2.58	0.92	1.64	0.74	1247.67	1.17	2301.45	4.33	30.79	1.35
2023.2	2.49	0.89	1.14	0.52	499.64	0.47	1795.66	3.38	25.01	1.35
2024.1	2.46	0.88	1.45	0.66	1373.96	1.29	2405.98	4.52	29.38	1.35
2024.2	2.09	0.75	1.99	0.91	1059.89	1.00	2089.47	3.93	26.71	1.35

Datasets

Training accurate predictors requires large datasets of synthesized neural networks. We used hls4ml to synthesize neural networks generated with parameters randomly sampled from predefined ranges (defaults of data classes in the code). Our models' training data is publicly available at https://borealisdata.ca/dataverse/rule4ml.

Newer predictors were trained on wa-hls4ml, a bigger dataset including more architectures and parameter ranges. This dataset, along with the HLS project files, can be found at https://huggingface.co/datasets/fastmachinelearning/wa-hls4ml and https://huggingface.co/datasets/fastmachinelearning/wa-hls4ml-projects.

Limitations

In their current iteration, the predictors can process Keras or PyTorch models to generate FPGA resources (BRAM, DSP, FF, LUT) and latency (Clock Cycles) estimations for various synthesis configurations. However, the training models are limited to specific layers: Dense/Linear, ReLU, Tanh, Sigmoid, Softmax, BatchNorm, Add, Concatenate, and Dropout. They are also constrained by synthesis parameters, notably clock_period (10 ns) and io_type (io_parallel). Inputs outside these configurations may result in inaccurate predictions.

License

This project is licensed under the GPL-3.0 License. See the LICENSE file for details.