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 826–850 of 4925 papers

TitleStatusHype
ARQ: A Mixed-Precision Quantization Framework for Accurate and Certifiably Robust DNNs—0
BitStack: Any-Size Compression of Large Language Models in Variable Memory EnvironmentsCode1
ALISE: Accelerating Large Language Model Serving with Speculative Scheduling—0
Accelerated AI Inference via Dynamic Execution Methods—0
A Comprehensive Study on Quantization Techniques for Large Language Models—0
GWQ: Gradient-Aware Weight Quantization for Large Language Models—0
APCodec+: A Spectrum-Coding-Based High-Fidelity and High-Compression-Rate Neural Audio Codec with Staged Training Paradigm—0
ELMGS: Enhancing memory and computation scaLability through coMpression for 3D Gaussian Splatting—0
IntLoRA: Integral Low-rank Adaptation of Quantized Diffusion ModelsCode1
Data Generation for Hardware-Friendly Post-Training QuantizationCode3
HRPVT: High-Resolution Pyramid Vision Transformer for medium and small-scale human pose estimation—0
The Impact of Inference Acceleration Strategies on Bias of LLMsCode0
NeuZip: Memory-Efficient Training and Inference with Dynamic Compression of Neural NetworksCode2
EoRA: Training-free Compensation for Compressed LLM with Eigenspace Low-Rank Approximation—0
Logarithmically Quantized Distributed Optimization over Dynamic Multi-Agent Networks—0
Vector Quantization Prompting for Continual LearningCode1
Unsupervised Panoptic Interpretation of Latent Spaces in GANs Using Space-Filling Vector QuantizationCode0
Unleashing Dynamic Range and Resolution in Unlimited Sensing Framework via Novel Hardware—0
DQRM: Deep Quantized Recommendation ModelsCode0
You Never Know: Quantization Induces Inconsistent Biases in Vision-Language Foundation Models—0
A Survey of Small Language Models—0
Content-Aware Radiance Fields: Aligning Model Complexity with Scene Intricacy Through Learned Bitwidth QuantizationCode0
COAT: Compressing Optimizer states and Activation for Memory-Efficient FP8 TrainingCode3
Learning ID-free Item Representation with Token Crossing for Multimodal Recommendation—0
Sliding DFT-based Signal Recovery for Modulo ADC with 1-bit Folding Information—0
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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