GATECH-EIC / Double-Win-QuantLinks
[ICML 2021] "Double-Win Quant: Aggressively Winning Robustness of Quantized DeepNeural Networks via Random Precision Training and Inference" by Yonggan Fu, Qixuan Yu, Meng Li, Vikas Chandra, Yingyan Lin
☆16Updated 3 years ago
Alternatives and similar repositories for Double-Win-Quant
Users that are interested in Double-Win-Quant are comparing it to the libraries listed below
Sorting:
- Any-Precision Deep Neural Networks (AAAI 2021)☆62Updated 5 years ago
- Qimera: Data-free Quantization with Synthetic Boundary Supporting Samples [NeurIPS 2021]☆34Updated 4 years ago
- [ICLR 2021] HW-NAS-Bench: Hardware-Aware Neural Architecture Search Benchmark☆114Updated 2 years ago
- ☆42Updated 3 years ago
- BSQ: Exploring Bit-Level Sparsity for Mixed-Precision Neural Network Quantization (ICLR 2021)☆42Updated 5 years ago
- ☆78Updated 3 years ago
- [Neurips 2021] Sparse Training via Boosting Pruning Plasticity with Neuroregeneration☆31Updated 2 years ago
- Official Implementation of Robustifying and Boosting Training-Free Neural Architecture Search☆10Updated last year
- This repo contains the code for studying the interplay between quantization and sparsity methods☆26Updated 11 months ago
- [NeurIPS 2020] "FracTrain: Fractionally Squeezing Bit Savings Both Temporally and Spatially for Efficient DNN Training" by Yonggan Fu, Ha…☆10Updated 3 years ago
- A collection of research papers on efficient training of DNNs☆70Updated 3 years ago
- ☆14Updated 2 years ago
- Code needed to reproduce results from my ICLR 2019 paper on fixed-point quantization of the backprop algorithm.☆10Updated 7 years ago
- ☆19Updated 4 years ago
- Official implementation of EMNLP'23 paper "Revisiting Block-based Quantisation: What is Important for Sub-8-bit LLM Inference?"☆24Updated 2 years ago
- The official PyTorch implementation of the ICLR2022 paper, QDrop: Randomly Dropping Quantization for Extremely Low-bit Post-Training Quan…☆127Updated 4 months ago
- Code for ICML 2021 submission☆35Updated 4 years ago
- [ECCV 2024] CLAMP-ViT: Contrastive Data-Free Learning for Adaptive Post-Training Quantization of ViTs☆18Updated last year
- The official implementation of TinyTrain [ICML '24]☆23Updated last year
- Pytorch implementation of our paper accepted by ICML 2023 -- "Bi-directional Masks for Efficient N:M Sparse Training"☆12Updated 2 years ago
- ☆27Updated last year
- Generic Neural Architecture Search via Regression (NeurIPS'21 Spotlight)☆36Updated 3 years ago
- AFP is a hardware-friendly quantization framework for DNNs, which is contributed by Fangxin Liu and Wenbo Zhao.☆13Updated 4 years ago
- ProxQuant: Quantized Neural Networks via Proximal Operators☆30Updated 6 years ago
- [IJCAI'22 Survey] Recent Advances on Neural Network Pruning at Initialization.☆59Updated 2 years ago
- ☆21Updated 3 years ago
- This project is the official implementation of our accepted ICLR 2022 paper BiBERT: Accurate Fully Binarized BERT.☆89Updated 2 years ago
- Official implementation for ECCV 2022 paper LIMPQ - "Mixed-Precision Neural Network Quantization via Learned Layer-wise Importance"☆61Updated 2 years ago
- ☆43Updated last year
- [NeurIPS 2023] ShiftAddViT: Mixture of Multiplication Primitives Towards Efficient Vision Transformer☆30Updated 2 years ago