model-optimization
larq
| model-optimization | larq | |
|---|---|---|
| 1 | 2 | |
| 1,470 | 692 | |
| 0.8% | 0.3% | |
| 6.8 | 7.5 | |
| 12 days ago | 8 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.
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).
larq
-
Running CNN on ATmega328P
You quantize the model parameters i.e., don't just send the model in which uses floating point math instead change it to fixed point. This has 2 advantages 1) a pure size reduction and 2) most low power MCU's don't have float point multipliers but do have single cycle fixed point multipliers. This is a classic DSP trick used for a long time. The real research aspects come-in as you start dropping below 8-bit; even coming down to single-bit in some cases(see Larq)
-
Simplifying AI to FPGA deployment, looking for opportunities
It is a difficult question. I work almost exclusively with open source, so I'm not much use to give you advice. Maybe you can see how Plumerai handles things -- they have some stuff proprietary, but they've also open-sourced their BNN Larq stuff: https://github.com/larq/larq
What are some alternatives?
deepsparse - Sparsity-aware deep learning inference runtime for CPUs
finn-examples - Dataflow QNN inference accelerator examples on FPGAs
qkeras - QKeras: a quantization deep learning library for Tensorflow Keras
data-science-ipython-notebooks - Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.
sparseml - Libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models
nngen - NNgen: A Fully-Customizable Hardware Synthesis Compiler for Deep Neural Network
3d-model-convert-to-gltf - Convert 3d model (STL/IGES/STEP/OBJ/FBX) to gltf and compression
aimet - AIMET is a library that provides advanced quantization and compression techniques for trained neural network models.
only_train_once - OTOv1-v3, NeurIPS, ICLR, TMLR, DNN Training, Compression, Structured Pruning, Erasing Operators, CNN, Diffusion, LLM
nncf - Neural Network Compression Framework for enhanced OpenVINO™ inference
Torch-Pruning - [CVPR 2023] Towards Any Structural Pruning; LLMs / SAM / Diffusion / Transformers / YOLOv8 / CNNs
Pretrained-Language-Model - Pretrained language model and its related optimization techniques developed by Huawei Noah's Ark Lab.