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 626–650 of 4925 papers

TitleStatusHype
A Survey on Large Language Model Acceleration based on KV Cache ManagementCode3
MBQ: Modality-Balanced Quantization for Large Vision-Language ModelsCode2
Causal Speech Enhancement with Predicting Semantics based on Quantized Self-supervised Learning Features—0
Semantic Residual for Multimodal Unified Discrete Representation—0
Advanced Knowledge Transfer: Refined Feature Distillation for Zero-Shot Quantization in Edge ComputingCode0
Resource-Efficient Transformer Architecture: Optimizing Memory and Execution Time for Real-Time Applications—0
Recommending Pre-Trained Models for IoT Devices—0
1.58-bit FLUX—0
Achieving Robustness in Blind Modulo Analog-to-Digital Conversion—0
Unified Stochastic Framework for Neural Network Quantization and Pruning—0
An Automatic Graph Construction Framework based on Large Language Models for RecommendationCode1
LSAQ: Layer-Specific Adaptive Quantization for Large Language Model Deployment—0
Highly Optimized Kernels and Fine-Grained Codebooks for LLM Inference on Arm CPUsCode0
GQSA: Group Quantization and Sparsity for Accelerating Large Language Model Inference—0
Hierarchical Vector Quantization for Unsupervised Action SegmentationCode1
The HalluRAG Dataset: Detecting Closed-Domain Hallucinations in RAG Applications Using an LLM's Internal StatesCode0
Adaptive Dataset Quantization—0
TCAQ-DM: Timestep-Channel Adaptive Quantization for Diffusion Models—0
Semantics Prompting Data-Free Quantization for Low-Bit Vision Transformers—0
Improving Quantization-aware Training of Low-Precision Network via Block Replacement on Full-Precision Counterpart—0
Log-Time K-Means Clustering for 1D Data: Novel Approaches with Proof and ImplementationCode0
MixLLM: LLM Quantization with Global Mixed-precision between Output-features and Highly-efficient System Design—0
Qua^2SeDiMo: Quantifiable Quantization Sensitivity of Diffusion Models—0
Preventing Local Pitfalls in Vector Quantization via Optimal TransportCode2
Progressive Fine-to-Coarse Reconstruction for Accurate Low-Bit Post-Training Quantization in Vision Transformers—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