SOTAVerified

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 AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 651–675 of 4925 papers

TitleStatusHype
Designing Large Foundation Models for Efficient Training and Inference: A SurveyCode1
ActNN: Reducing Training Memory Footprint via 2-Bit Activation Compressed TrainingCode1
BinaryDM: Accurate Weight Binarization for Efficient Diffusion ModelsCode1
Binary Latent DiffusionCode1
Learning Discrete Representations via Constrained Clustering for Effective and Efficient Dense RetrievalCode1
Compression with Bayesian Implicit Neural RepresentationsCode1
Learnable Lookup Table for Neural Network QuantizationCode1
Learned Step Size QuantizationCode1
COMQ: A Backpropagation-Free Algorithm for Post-Training QuantizationCode1
DiTAS: Quantizing Diffusion Transformers via Enhanced Activation SmoothingCode1
Learning Architectures for Binary NetworksCode1
Diverse Sample Generation: Pushing the Limit of Generative Data-free QuantizationCode1
Learning from Students: Applying t-Distributions to Explore Accurate and Efficient Formats for LLMsCode1
Compressing LLMs: The Truth is Rarely Pure and Never SimpleCode1
Hybrid Inverted Index Is a Robust Accelerator for Dense RetrievalCode1
DNN+NeuroSim V2.0: An End-to-End Benchmarking Framework for Compute-in-Memory Accelerators for On-chip TrainingCode1
EA-3DGS: Efficient and Adaptive 3D Gaussians with Highly Enhanced Quality for outdoor scenesCode1
N3H-Core: Neuron-designed Neural Network Accelerator via FPGA-based Heterogeneous Computing CoresCode1
Catastrophic Failure of LLM Unlearning via QuantizationCode1
Language Embedded 3D Gaussians for Open-Vocabulary Scene UnderstandingCode1
Compress Any Segment Anything Model (SAM)Code1
LaCo: Large Language Model Pruning via Layer CollapseCode1
DQS3D: Densely-matched Quantization-aware Semi-supervised 3D DetectionCode1
Network Binarization via Contrastive LearningCode1
Benchmarking of DL Libraries and Models on Mobile DevicesCode1
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All datasetsImageNetCIFAR-10Wiki-40BAgeDB-30CFP-FPCOCO (Common Objects in Context)IJB-BIJB-CKnowledge-based:LFW

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1FQ-ViT (ViT-L)Top-1 Accuracy (%)85.03—Unverified
2FQ-ViT (ViT-B)Top-1 Accuracy (%)83.31—Unverified
3FQ-ViT (Swin-B)Top-1 Accuracy (%)82.97—Unverified
4FQ-ViT (Swin-S)Top-1 Accuracy (%)82.71—Unverified
5FQ-ViT (DeiT-B)Top-1 Accuracy (%)81.2—Unverified
6FQ-ViT (Swin-T)Top-1 Accuracy (%)80.51—Unverified
7FQ-ViT (DeiT-S)Top-1 Accuracy (%)79.17—Unverified
8Xception W8A8Top-1 Accuracy (%)78.97—Unverified
9ADLIK-MO-ResNet50-W4A4Top-1 Accuracy (%)77.88—Unverified
10ADLIK-MO-ResNet50-W3A4Top-1 Accuracy (%)77.34—Unverified
#ModelMetricClaimedVerifiedStatus
13DCNN_VIVA_3MAP160,327.04—Unverified
2DTQMAP0.79—Unverified
#ModelMetricClaimedVerifiedStatus
1OutEffHop-Bert_basePerplexity6.3—Unverified
2OutEffHop-Bert_basePerplexity6.21—Unverified
#ModelMetricClaimedVerifiedStatus
1Accuracy98.13—Unverified
#ModelMetricClaimedVerifiedStatus
1Accuracy92.92—Unverified
#ModelMetricClaimedVerifiedStatus
1SSD ResNet50 V1 FPN 640x640MAP34.3—Unverified
#ModelMetricClaimedVerifiedStatus
1TAR @ FAR=1e-495.13—Unverified
#ModelMetricClaimedVerifiedStatus
1TAR @ FAR=1e-496.38—Unverified
#ModelMetricClaimedVerifiedStatus
13DCNN_VIVA_5All84,809,664—Unverified
#ModelMetricClaimedVerifiedStatus
1Accuracy99.8—Unverified