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 26–50 of 4925 papers

TitleStatusHype
Q-GaLore: Quantized GaLore with INT4 Projection and Layer-Adaptive Low-Rank GradientsCode5
Autoregressive Image Generation without Vector QuantizationCode5
SpinQuant: LLM quantization with learned rotationsCode5
PV-Tuning: Beyond Straight-Through Estimation for Extreme LLM CompressionCode5
SQUAT: Stateful Quantization-Aware Training in Recurrent Spiking Neural NetworksCode5
Extreme Compression of Large Language Models via Additive QuantizationCode5
CacheGen: KV Cache Compression and Streaming for Fast Large Language Model ServingCode5
QA-LoRA: Quantization-Aware Low-Rank Adaptation of Large Language ModelsCode5
MoVQ: Modulating Quantized Vectors for High-Fidelity Image GenerationCode5
YOLOv6: A Single-Stage Object Detection Framework for Industrial ApplicationsCode5
LLM.int8(): 8-bit Matrix Multiplication for Transformers at ScaleCode5
BigDL 2.0: Seamless Scaling of AI Pipelines from Laptops to Distributed ClusterCode5
Scaling Law for Quantization-Aware TrainingCode4
UniTok: A Unified Tokenizer for Visual Generation and UnderstandingCode4
Autoregressive Video Generation without Vector QuantizationCode4
Taming Scalable Visual Tokenizer for Autoregressive Image GenerationCode4
SVDQuant: Absorbing Outliers by Low-Rank Components for 4-Bit Diffusion ModelsCode4
BitNet a4.8: 4-bit Activations for 1-bit LLMsCode4
SNAC: Multi-Scale Neural Audio CodecCode4
DuoAttention: Efficient Long-Context LLM Inference with Retrieval and Streaming HeadsCode4
Restructuring Vector Quantization with the Rotation TrickCode4
VPTQ: Extreme Low-bit Vector Post-Training Quantization for Large Language ModelsCode4
T-MAC: CPU Renaissance via Table Lookup for Low-Bit LLM Deployment on EdgeCode4
LLMC: Benchmarking Large Language Model Quantization with a Versatile Compression ToolkitCode4
QServe: W4A8KV4 Quantization and System Co-design for Efficient LLM ServingCode4
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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