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

Showing 201250 of 4925 papers

TitleStatusHype
LLM-FP4: 4-Bit Floating-Point Quantized TransformersCode2
D2GV: Deformable 2D Gaussian Splatting for Video Representation in 400FPSCode2
Low-Rank Quantization-Aware Training for LLMsCode2
AdaBM: On-the-Fly Adaptive Bit Mapping for Image Super-ResolutionCode2
LeanVec: Searching vectors faster by making them fitCode2
LiDAR-PTQ: Post-Training Quantization for Point Cloud 3D Object DetectionCode2
KV Cache Compression, But What Must We Give in Return? A Comprehensive Benchmark of Long Context Capable ApproachesCode2
Binarized Neural Machine TranslationCode2
QQQ: Quality Quattuor-Bit Quantization for Large Language ModelsCode2
A Spark of Vision-Language Intelligence: 2-Dimensional Autoregressive Transformer for Efficient Finegrained Image GenerationCode2
Jetfire: Efficient and Accurate Transformer Pretraining with INT8 Data Flow and Per-Block QuantizationCode2
Quantized symbolic time series approximationCode2
QUEEN: QUantized Efficient ENcoding of Dynamic Gaussians for Streaming Free-viewpoint VideosCode2
Binary Neural Networks: A SurveyCode2
Dataset QuantizationCode2
LightGaussian: Unbounded 3D Gaussian Compression with 15x Reduction and 200+ FPSCode2
MAexp: A Generic Platform for RL-based Multi-Agent ExplorationCode2
OmniQuant: Omnidirectionally Calibrated Quantization for Large Language ModelsCode2
BitNet: Scaling 1-bit Transformers for Large Language ModelsCode2
Imp: Highly Capable Large Multimodal Models for Mobile DevicesCode2
Q-VLM: Post-training Quantization for Large Vision-Language ModelsCode2
any4: Learned 4-bit Numeric Representation for LLMsCode2
Any-Precision LLM: Low-Cost Deployment of Multiple, Different-Sized LLMsCode2
An empirical study of LLaMA3 quantization: from LLMs to MLLMsCode2
hls4ml: An Open-Source Codesign Workflow to Empower Scientific Low-Power Machine Learning DevicesCode2
I-BERT: Integer-only BERT QuantizationCode2
INT-FlashAttention: Enabling Flash Attention for INT8 QuantizationCode2
Bolt: Accelerated Data Mining with Fast Vector CompressionCode2
HAQ: Hardware-Aware Automated Quantization with Mixed PrecisionCode2
Scaling the Codebook Size of VQGAN to 100,000 with a Utilization Rate of 99%Code2
Harmonizing Visual Representations for Unified Multimodal Understanding and GenerationCode2
An Empirical Study of Qwen3 QuantizationCode2
Similarity search in the blink of an eye with compressed indicesCode2
SimLayerKV: A Simple Framework for Layer-Level KV Cache ReductionCode2
Compressing Volumetric Radiance Fields to 1 MBCode2
GPTAQ: Efficient Finetuning-Free Quantization for Asymmetric CalibrationCode2
GLARE: Low Light Image Enhancement via Generative Latent Feature based Codebook RetrievalCode2
SpeechTokenizer: Unified Speech Tokenizer for Speech Large Language ModelsCode2
GuidedQuant: Large Language Model Quantization via Exploiting End Loss GuidanceCode2
I-ViT: Integer-only Quantization for Efficient Vision Transformer InferenceCode2
From Tiny Machine Learning to Tiny Deep Learning: A SurveyCode2
Efficient Multi-Vector Dense Retrieval Using Bit VectorsCode2
AnalogNAS-Bench: A NAS Benchmark for Analog In-Memory ComputingCode2
GaussianToken: An Effective Image Tokenizer with 2D Gaussian SplattingCode2
CompGS: Smaller and Faster Gaussian Splatting with Vector QuantizationCode2
Efficient Speech Language Modeling via Energy Distance in Continuous Latent SpaceCode2
Compact 3D Gaussian Representation for Radiance FieldCode2
FlowSE: Efficient and High-Quality Speech Enhancement via Flow MatchingCode2
GEAR: An Efficient KV Cache Compression Recipe for Near-Lossless Generative Inference of LLMCode2
Fast convolutional neural networks on FPGAs with hls4mlCode2
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Benchmark Results

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