Merge pull request #57 from shivmgg/master

Update README: added paper on minifloat quantization

README.md

@@ -111,6 +111,7 @@ Amir Gholami\* , Sehoon Kim\* , Zhen Dong\* , Zhewei Yao\* , Michael W. Mahoney,
 
 ### 2024
 
+- [[arXiv](https://arxiv.org/abs/2311.12359)] Post-Training Quantization with Low-precision Minifloats and Integers on FPGAs [[code](https://github.com/Xilinx/brevitas/tree/dev/src/brevitas_examples/imagenet_classification/ptq)][__`hardware`__]
 - [[arXiv](https://arxiv.org/abs/2404.14047)] How Good Are Low-bit Quantized LLaMA3 Models? An Empirical Study [[code](https://github.com/Macaronlin/LLaMA3-Quantization)]![GitHub Repo stars](https://img.shields.io/github/stars/Macaronlin/LLaMA3-Quantization) [[HuggingFace](https://huggingface.co/LLMQ)]
 - [[arXiv](https://arxiv.org/abs/2402.05445)] Accurate LoRA-Finetuning Quantization of LLMs via Information Retention [[code](https://github.com/htqin/IR-QLoRA)]![GitHub Repo stars](https://img.shields.io/github/stars/htqin/IR-QLoRA)
 - [[arXiv](https://arxiv.org/abs/2402.04291)] BiLLM: Pushing the Limit of Post-Training Quantization for LLMs [[code](https://github.com/Aaronhuang-778/BiLLM)]![GitHub Repo stars](https://img.shields.io/github/stars/Aaronhuang-778/BiLLM)
@@ -627,7 +628,7 @@ Amir Gholami\* , Sehoon Kim\* , Zhen Dong\* , Zhewei Yao\* , Michael W. Mahoney,
 - [[CVPR](https://openaccess.thecvf.com/content_cvpr_2017/papers/Cai_Deep_Learning_With_CVPR_2017_paper.pdf)] [251:fire:] Deep Learning with Low Precision by Half-wave Gaussian Quantization. [**`qnn`**] [[code](https://github.com/zhaoweicai/hwgq)] [118:star:]
 - [[CVPR](https://openaccess.thecvf.com/content_cvpr_2017/papers/Juefei-Xu_Local_Binary_Convolutional_CVPR_2017_paper.pdf)] [156:fire:] Local Binary Convolutional Neural Networks. [**`bnn`**] [[torch](https://github.com/juefeix/lbcnn.torch)] [94:star:]
 - [[FPGA](https://arxiv.org/abs/1612.07119)] [463:fire:] FINN: A Framework for Fast, Scalable Binarized Neural Network Inference. [**`hardware`**] [**`bnn`**]
-- [[ICASSP](https://arxiv.org/abs/1702.08171))] Fixed-point optimization of deep neural networks with adaptive step size retraining. [**`qnn`**]
+- [[ICASSP](https://arxiv.org/abs/1702.08171)] Fixed-point optimization of deep neural networks with adaptive step size retraining. [**`qnn`**]
 - [[ICCV](https://openaccess.thecvf.com/content_ICCV_2017/papers/Bulat_Binarized_Convolutional_Landmark_ICCV_2017_paper.pdf)] [130:fire:] Binarized Convolutional Landmark Localizers for Human Pose Estimation and Face Alignment with Limited Resources. [**`bnn`**] [[homepage](https://www.adrianbulat.com/binary-cnn-landmarks)] [[torch](https://github.com/1adrianb/binary-human-pose-estimation)] [207:star:]
 - [[ICCV](https://openaccess.thecvf.com/content_ICCV_2017/papers/Li_Performance_Guaranteed_Network_ICCV_2017_paper.pdf)] [55:fire:] Performance Guaranteed Network Acceleration via High-Order Residual Quantization. [**`qnn`**]
 - [[ICLR](https://openreview.net/pdf?id=HyQJ-mclg)] [554:fire:] Incremental Network Quantization: Towards Lossless CNNs with Low-Precision Weights. [**`qnn`**] [[torch](https://github.com/Mxbonn/INQ-pytorch)] [144:star:]
@@ -647,7 +648,7 @@ Amir Gholami\* , Sehoon Kim\* , Zhen Dong\* , Zhewei Yao\* , Michael W. Mahoney,
 
 - [[CoRR](http:https://arxiv.org/abs/1606.06160)] [1k:fire:] DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients. [**`qnn`**] [[code](https://github.com/tensorpack/tensorpack/tree/master/examples/DoReFa-Net)] [5.8k:star:]
 - [[ECCV](https://arxiv.org/abs/1603.05279)] [2.7k:fire:] XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks. [**`bnn`**] [[torch](https://github.com/allenai/XNOR-Net)] [787:star:]
-- [[ICASSP](https://arxiv.org/abs/1512.01322))] Fixed-point Performance Analysis of Recurrent Neural Networks. [**`qnn`**]
+- [[ICASSP](https://arxiv.org/abs/1512.01322)] Fixed-point Performance Analysis of Recurrent Neural Networks. [**`qnn`**]
 - [[NeurIPS](https://arxiv.org/pdf/1605.04711.pdf)] [572:fire:] Ternary weight networks. [**`qnn`**] [[code](https://github.com/fengfu-chris/caffe-twns)] [61:star:]
 - [[NeurIPS](https://arxiv.org/pdf/1602.02830)] [1.7k:fire:] Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1. [**`bnn`**] [[torch](https://github.com/itayhubara/BinaryNet)] [239:star:]
 - [[CVPR](https://openaccess.thecvf.com/content_cvpr_2016/html/Wu_Quantized_Convolutional_Neural_CVPR_2016_paper.html)] [270:fire:] Quantized convolutional neural networks for mobile devices. [code](https://github.com/jiaxiang-wu/quantized-cnn)