conifer
qkeras
conifer | qkeras | |
---|---|---|
1 | 3 | |
40 | 522 | |
- | 1.1% | |
6.9 | 6.6 | |
3 months ago | about 2 months ago | |
Python | Python | |
Apache License 2.0 | Apache License 2.0 |
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conifer
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Random Forest: how are predictions related to node values?
I came across a useful package ( https://github.com/thesps/conifer), but it does not support multiclass classification for RF.
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?
temporal-shift-module - [ICCV 2019] TSM: Temporal Shift Module for Efficient Video Understanding
model-optimization - A toolkit to optimize ML models for deployment for Keras and TensorFlow, including quantization and pruning.
python-socketio - Python Socket.IO server and client
hls4ml - Machine learning on FPGAs using HLS
fusesoc - Package manager and build abstraction tool for FPGA/ASIC development
aimet - AIMET is a library that provides advanced quantization and compression techniques for trained neural network models.
conifer - Collect and revisit web pages.
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.
Keras - Deep Learning for humans
route-bender-4000
finn - Dataflow compiler for QNN inference on FPGAs