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666DZY666 / micronet

Star 1.8k

micronet, a model compression and deploy lib. compression: 1、quantization: quantization-aware-training(QAT), High-Bit(>2b)(DoReFa/Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference)、Low-Bit(≤2b)/Ternary and Binary(TWN/BNN/XNOR-Net); post-training-quantization(PTQ), 8-bit(tensorrt); 2、 pruning: normal、regular and group convolutional channel pruning; 3、 group convolution structure; 4、batch-normalization fuse for quantization. deploy: tensorrt, fp32/fp16/int8(ptq-calibration)、op-adapt(upsample)、dynamic_shape

pytorch pruning convolutional-networks quantization xnor-net tensorrt model-compression bnn neuromorphic-computing group-convolution onnx network-in-network tensorrt-int8-python dorefa twn network-slimming integer-arithmetic-only quantization-aware-training post-training-quantization batch-normalization-fuse
  • Updated Oct 6, 2021
  • Python

quic / aimet

Star 1.1k
Open

Update TF Bias Correction to use new Quantsim implementation

1
quic-ssiddego
quic-ssiddego commented Jul 16, 2021
  • Deprecate the usage of old quantsim implementation
  • Update bias correction implementation to use new quantsim api

If you are interested in working on this issue - please indicate via a comment on this issue. It should be possible for us to pair you up with an existing contributor to help you get started.

From a complexity perspective, this ticket is at an easy level.

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good first issue Easy
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guan-yuan / awesome-AutoML-and-Lightweight-Models

Star 774

A list of high-quality (newest) AutoML works and lightweight models including 1.) Neural Architecture Search, 2.) Lightweight Structures, 3.) Model Compression, Quantization and Acceleration, 4.) Hyperparameter Optimization, 5.) Automated Feature Engineering.

tensorflow pytorch hyperparameter-optimization awesome-list quantization nas automl model-compression neural-architecture-search meta-learning architecture-search quantized-training model-acceleration automated-feature-engineering quantized-neural-network
  • Updated Jun 19, 2021

intel / neural-compressor

Star 415

Intel® Neural Compressor (formerly known as Intel® Low Precision Optimization Tool), targeting to provide unified APIs for network compression technologies, such as low precision quantization, sparsity, pruning, knowledge distillation, across different deep learning frameworks to pursue optimal inference performance.

sparsity deep-learning pruning quantization knowledge-distillation auto-tuning low-precision quantization-aware-training post-training-quantization
  • Updated Jul 8, 2022
  • Python

Xilinx / finn

Star 406
Open

Check Vivado version

maltanar
maltanar commented Mar 2, 2020

FINN has a Vivado version requirements, e.g. 2019.1 in the 0.2b release. The available Vivado version should be checked before any Vivado-related commands are launched, and an assertion should be raised if there is a version mismatch.

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enhancement good first issue

SforAiDl / KD_Lib

Star 401
Open

Benchmarking Pruning and Quantization

avishreekh
avishreekh commented May 7, 2021

We also need to benchmark the Lottery-tickets Pruning algorithm and the Quantization algorithms. The models used for this would be the student networks discussed in #105 (ResNet18, MobileNet v2, Quantization v2).

Pruning (benchmark upto 40, 50 and 60 % pruned weights)

  • Lottery Tickets

Quantization

  • Static
  • QAT
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help wanted good first issue Priority: High

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