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High Granularity Quantization v2 [FPGA'26] [FCCM'26, best paper]

HGQ2 최신버젼 다운로드

최종 버전 다운로드 (.zip)

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HGQ2: High Granularity Quantization 2
=============================================

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HGQ2 (High Granularity Quantization 2) is a quantization-aware training framework built on Keras v3, targeting real-time deep learning applications on edge devices like FPGAs. It provides a comprehensive set of tools for creating and training quantized neural networks with minimal effort.

HGQ2 implements an gradient-based automatic bitwidth optimization and quantization-aware training algorithm. By laveraging gradients, it allows for bitwidth optimization at arbitrary granularity, up to per-weight and per-activation level.

- **High Granularity**: HGQ supports per-weight and per-activation bitwidth optimization, or any other lower granularity.
- **Automatic Quantization**: Bit-widths are optimized via gradients, no need to manually tune them in general.
- **What you see is what you get**: One get exactly what you get from `Keras` models from `RTL` models.
  - still subject to machine float precision limitation.
- **Accurate Resource Estimation**: `EBOPs` estimated by HGQ gives a good indication of the actual resource usage on FPGA, either upper limit of `LUT` (`da4ml`) or `LUT + 55 * DSP` (`hls4ml`).

In addition, this framework improves upon the old HGQ implementation in the following aspects:

- **Scalability**: HGQ2 supports `TensorFlow`, `JAX`, and `PyTorch`. As XLA compilation in`JAX` and `TensorFlow` can significantly speed up the training process. Training speed on HGQ2 can be 1.2-5 times faster than the previous implementation.
- **Quantizers**:
  - _Fixed-point_: While the last implementation only optimizes the number of floating bits with one way of parameterizing the fixed-point numbers, HGQ2 supports multiple ways of parametrizing them, and allows of optimizing any part of them via gradients.
  - _Minifloat_: Training with minifloat quantization is supported, also with surrogate gradients support (alpha quality).
- **More Layers**: More layers are supported now, including the powerful `EinsumDense(BatchNorm)` layer and the `MultiHeadAttention` layer with bit-accurate softmax and scaled dot-product attention.

## Installation

```bash
pip install HGQ2
```

If you are using `da4ml`, please make sure it is at least version 0.6:

```bash
pip install da4ml>=0.6
```

If you are using `hls4ml`, please make sure it is at least version 1.2:

```bash
pip install hls4ml>=1.2.0
```

## Usage

Please refer to the [documentation](https://calad0i.github.io/HGQ2/) for more details on how to use the library.

A minimal example is shown below:

```python
   import keras
   from hgq.layers import QDense, QConv2D
   from hgq.config import LayerConfigScope, QuantizerConfigScope

# Setup quantization configuration
   # These values are the defaults, just for demonstration purposes here
   with (
      # Configuration scope for setting the default quantization type and overflow mode
      # The second configuration scope overrides the first one for the 'datalane' place
      QuantizerConfigScope(place='all', default_q_type='kbi', overflow_mode='SAT_SYM'),
      # Configuration scope for enabling EBOPs and setting the beta0 value
      QuantizerConfigScope(place='datalane', default_q_type='kif', overflow_mode='WRAP'),
      LayerConfigScope(enable_ebops=True, beta0=1e-5),
   ):
      model = keras.Sequential([
         QConv2D(32, (3, 3), activation='relu'),
         keras.layers.MaxPooling2D((2, 2)),
         keras.layers.Flatten(),
         QDense(10)
      ])
```

## Citation

If you use HGQ2 in your research, please consider citing the following paper:

```bibtex
@inproceedings{hgq,
  author = {Sun, Chang and Que, Zhiqiang and Aarrestad, Thea and Loncar, Vladimir and Ngadiuba, Jennifer and Luk, Wayne and Spiropulu, Maria},
  title = {HGQ: High Granularity Quantization for Real-time Neural Networks on FPGAs},
  year = {2026},
  isbn = {9798400720796},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  url = {https://doi.org/10.1145/3748173.3779200},
  doi = {10.1145/3748173.3779200},
  booktitle = {Proceedings of the 2026 ACM/SIGDA International Symposium on Field Programmable Gate Arrays},
  pages = {79–91},
  numpages = {13},
  keywords = {quantization-aware training, fpga, real-time inference, neural networks, hardware-software codesign, low-latency, quantization},
  location = {USA},
  series = {FPGA '26}
}
```

If LUT-based layers are used, please also cite the following paper:

```bibtex
@inproceedings{hgq-lut,
  author={Sun, Chang and Que, Zhiqiang and Zadeh, Bakhtiar and Liu, Qibin and Alvarez, Kevin H. and Luk, Wayne and Spiropulu, Maria},
  booktitle={2026 IEEE 34th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM)},
  title={HGQ-LUT: Fast LUT-Aware Training and Efficient Architectures for DNN Inference},
  year={2026},
  volume={},
  number={},
  pages={19-28},
  keywords={Modeling;Table lookup;Training;Field programmable gate arrays;Timing;Manuals;Hardware;Accuracy;Neural networks;Elementary particles;machine learning;lookup-based inference;quantization;co-design;fpga},
  doi={10.1109/FCCM68464.2026.00017},
  series = {FCCM '26}
}
```

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