model-optimization
qkeras
model-optimization | qkeras | |
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1 | 3 | |
1,470 | 523 | |
0.8% | 1.1% | |
6.8 | 6.2 | |
12 days ago | 4 days ago | |
Python | Python | |
Apache License 2.0 | Apache License 2.0 |
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model-optimization
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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).
qkeras
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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.
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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
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[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
What are some alternatives?
deepsparse - Sparsity-aware deep learning inference runtime for CPUs
hls4ml - Machine learning on FPGAs using HLS
sparseml - Libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models
aimet - AIMET is a library that provides advanced quantization and compression techniques for trained neural network models.
3d-model-convert-to-gltf - Convert 3d model (STL/IGES/STEP/OBJ/FBX) to gltf and compression
conifer - Collect and revisit web pages.
horovod - Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.
larq - An Open-Source Library for Training Binarized Neural Networks
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.
only_train_once - OTOv1-v3, NeurIPS, ICLR, TMLR, DNN Training, Compression, Structured Pruning, Erasing Operators, CNN, Diffusion, LLM
Keras - Deep Learning for humans