Quantization

Quantization is a promising technique to reduce the computation cost of neural network training, which can replace high-cost floating-point numbers (e.g., float32) with low-cost fixed-point numbers (e.g., int8/int16).

Source: Adaptive Precision Training: Quantify Back Propagation in Neural Networks with Fixed-point Numbers

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 151–160 of 4925 papers

Title	Date	Tasks	Status	Hype	Score
Jetfire: Efficient and Accurate Transformer Pretraining with INT8 Data Flow and Per-Block Quantization	Mar 19, 2024	Quantization	CodeCode Available	2	5
I-ViT: Integer-only Quantization for Efficient Vision Transformer Inference	Jul 4, 2022	Quantization	CodeCode Available	2	5
KV Cache Compression, But What Must We Give in Return? A Comprehensive Benchmark of Long Context Capable Approaches	Jul 1, 2024	Book summarizationQuantization	CodeCode Available	2	5
LightGaussian: Unbounded 3D Gaussian Compression with 15x Reduction and 200+ FPS	Nov 28, 2023	Knowledge DistillationNeRF	CodeCode Available	2	5
MAexp: A Generic Platform for RL-based Multi-Agent Exploration	Apr 19, 2024	DiversityMulti-agent Reinforcement Learning	CodeCode Available	2	5
Imp: Highly Capable Large Multimodal Models for Mobile Devices	May 20, 2024	QuantizationVisual Question Answering	CodeCode Available	2	5
Any-Precision LLM: Low-Cost Deployment of Multiple, Different-Sized LLMs	Feb 16, 2024	Quantization	CodeCode Available	2	5
any4: Learned 4-bit Numeric Representation for LLMs	Jul 7, 2025	GPUGSM8K	CodeCode Available	2	5
An empirical study of LLaMA3 quantization: from LLMs to MLLMs	Apr 22, 2024	Language ModellingLarge Language Model	CodeCode Available	2	5
hls4ml: An Open-Source Codesign Workflow to Empower Scientific Low-Power Machine Learning Devices	Mar 9, 2021	BIG-bench Machine LearningDiagnostic	CodeCode Available	2	5

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All datasets ImageNet CIFAR-10 Wiki-40B AgeDB-30 CFP-FP COCO (Common Objects in Context)IJB-B IJB-C Knowledge-based:LFW

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	FQ-ViT (ViT-L)	Top-1 Accuracy (%)	85.03	—	Unverified
2	FQ-ViT (ViT-B)	Top-1 Accuracy (%)	83.31	—	Unverified
3	FQ-ViT (Swin-B)	Top-1 Accuracy (%)	82.97	—	Unverified
4	FQ-ViT (Swin-S)	Top-1 Accuracy (%)	82.71	—	Unverified
5	FQ-ViT (DeiT-B)	Top-1 Accuracy (%)	81.2	—	Unverified
6	FQ-ViT (Swin-T)	Top-1 Accuracy (%)	80.51	—	Unverified
7	FQ-ViT (DeiT-S)	Top-1 Accuracy (%)	79.17	—	Unverified
8	Xception W8A8	Top-1 Accuracy (%)	78.97	—	Unverified
9	ADLIK-MO-ResNet50-W4A4	Top-1 Accuracy (%)	77.88	—	Unverified
10	ADLIK-MO-ResNet50-W3A4	Top-1 Accuracy (%)	77.34	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	3DCNN_VIVA_3	MAP	160,327.04	—	Unverified
2	DTQ	MAP	0.79	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	OutEffHop-Bert_base	Perplexity	6.3	—	Unverified
2	OutEffHop-Bert_base	Perplexity	6.21	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1		Accuracy	98.13	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1		Accuracy	92.92	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	SSD ResNet50 V1 FPN 640x640	MAP	34.3	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1		TAR @ FAR=1e-4	95.13	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1		TAR @ FAR=1e-4	96.38	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	3DCNN_VIVA_5	All	84,809,664	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1		Accuracy	99.8	—	Unverified