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 521–530 of 4925 papers

Title	Date	Tasks	Status	Hype
Effectiveness of self-supervised pre-training for speech recognition	Nov 10, 2019	Language ModellingQuantization	CodeCode Available	1
And the Bit Goes Down: Revisiting the Quantization of Neural Networks	Jul 12, 2019	CPUQuantization	CodeCode Available	1
Anchor-based Plain Net for Mobile Image Super-Resolution	May 20, 2021	Image Super-ResolutionQuantization	CodeCode Available	1
ActNN: Reducing Training Memory Footprint via 2-Bit Activation Compressed Training	Apr 29, 2021	Quantization	CodeCode Available	1
An Automatic Graph Construction Framework based on Large Language Models for Recommendation	Dec 24, 2024	graph constructionQuantization	CodeCode Available	1
Active Image Indexing	Oct 5, 2022	Copy DetectionQuantization	CodeCode Available	1
Catastrophic Failure of LLM Unlearning via Quantization	Oct 21, 2024	Machine UnlearningQuantization	CodeCode Available	1
Injecting Domain Adaptation with Learning-to-hash for Effective and Efficient Zero-shot Dense Retrieval	May 23, 2022	Ad-Hoc Information RetrievalCPU	CodeCode Available	1
DNN+NeuroSim V2.0: An End-to-End Benchmarking Framework for Compute-in-Memory Accelerators for On-chip Training	Mar 13, 2020	BenchmarkingQuantization	CodeCode Available	1
Active-Dormant Attention Heads: Mechanistically Demystifying Extreme-Token Phenomena in LLMs	Oct 17, 2024	Quantization	CodeCode Available	1

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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