qkeras
model-optimization
| qkeras | model-optimization | |
|---|---|---|
| 3 | 1 | |
| 522 | 1,470 | |
| 1.1% | 0.8% | |
| 6.6 | 6.8 | |
| about 2 months ago | 10 days ago | |
| Python | Python | |
| Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
qkeras
-
How to build FPGA-based ML accelerator?
I would check out hls4ml. It's an open source project made by/for people at CERN to convert neural networks created in Python using QKeras (a quantization extension of Keras) into HLS, with Vivado HLS being the most well supported. There are some caveats though, and a fellow student and I have had trouble getting the generated HLS to match the Keras model and be feasible to synthesize, but it seems to work well for smaller neural networks.
-
FPGA Neural Network
For quantization-aware training, there's also a tool we integrate with called qkeras: https://github.com/google/qkeras/tree/master/qkeras
-
[D] How to Quantize a CNN; And how to deal with a professor...
Brevitas appears to be what you're looking for. I haven't used that but developed something similar myself for a previous project. You could take a look at https://github.com/google/qkeras too
model-optimization
-
Need Help With Pruning Model Weights in Tensorflow 2
I have been following the example shown here, and so far I've had mixed results and wanted to ask for some help because the resources I've found online have not been able to answer some of my questions (perhaps because some of these are obvious and I am just being dumb).
What are some alternatives?
hls4ml - Machine learning on FPGAs using HLS
deepsparse - Sparsity-aware deep learning inference runtime for CPUs
aimet - AIMET is a library that provides advanced quantization and compression techniques for trained neural network models.
sparseml - Libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models
conifer - Collect and revisit web pages.
3d-model-convert-to-gltf - Convert 3d model (STL/IGES/STEP/OBJ/FBX) to gltf and compression
horovod - Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.
d2l-en - Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
larq - An Open-Source Library for Training Binarized Neural Networks
Keras - Deep Learning for humans
only_train_once - OTOv1-v3, NeurIPS, ICLR, TMLR, DNN Training, Compression, Structured Pruning, Erasing Operators, CNN, Diffusion, LLM