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April 1, 2024
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Quantization timeline
The quantization development history
Created by
龚成
⟶ Updated 29 Mar 2020 ⟶
List of edits
Timelines by
龚成
:
30 Aug 2019
1
0
462
Pruning
30 Aug 2019
0
0
363
knowledge transfer/ distillation
30 Aug 2019
0
0
319
decomposation
22 Aug 2019
0
0
272
New timeline
12 Nov 2019
0
0
239
Pruning Entropy
22 Jan 2020
0
0
175
New timeline
Comments
Events
Han Song: Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding, 2015
M Courbariaux: Binarized neural networks: Training deep neural networks with weights and activations constrained to+ 1 or-1 (BNNs)
Ternary Weight Networks (TWNs)
Trained Ternary Quantization (TTQ)
Training and Inference with Integers in Deep Neural Networks
HAQ: Hardware-Aware Automated Quantization With Mixed Precision, CVPR, 2019
Extremely Low Bit Neural Network Squeeze the Last Bit Out with ADMM, AAAI, 2018
Dorefa-Net: training low-bitwidth CNN with low-bitwidth gradients
CLIP-Q: Deep Network Compression Learning by In-Parallel Pruning- Quantization (CVPR 2018)
Ternary Neural Networks for Resource-Efficient AI Applications (TNNs)
Philipp Gysel: Hardware-oriented Approximation of Convolutional Neural Networks
Canran Jin: Sparse Ternary Connect: Convolutional Neural Networks Using Ternarized Weights with Enhanced Sparsity (STC)
ChengGong: $\mu$L2Q: An Ultra-Low Loss Quantization Method for DNN
Bichen Wu: Mixed precision quantization of convnets via differentiable neural architecture search, 2018
Lei Deng: GXNOR-Net: Training deep neural networks with ternary weights and activations without full-precision memory under a unified discretization framework, NN, 2018
Rastegari: XNOR-Net: Imagenet classification using binary convolutional neural networks, ECCV, 2016
Deep Learning with Low Precision by Half-wave Gaussian Quantization (HWGQ)
Deep Neural Network Compression with Single and Multiple Level Quantization
Two-Step Quantization for Low-bit Neural Networks, CVPR, 2018
Incremental Network Quantization: Towards Lossless CNNs with Low- Precision Weights, INQ, 2017
Yao Chen: T-DLA: An Open-source Deep Learning Accelerator for Ternarized DNN Models on Embedded FPGA, 2019
Daisuke Miyashita: Convolutional Neural Networks using Logarithmic Data Representation
Forward and Backward Information Retention for Accurate Binary Neural Networks, CVPR 2020
Retrain-Less Weight Quantization for Multiplier-Less Convolutional Neural Networks
Improving Neural Network Quantization without Retraining using Outlier Channel Splitting, arxiv
“Binaryconnect: Training deep neural networks with binary weights during propagations, NIPS
Neural networks with few multiplications
QAT: Quantization and training of neural networks for efficient integer-arithmetic-only inference, CVPR