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 1201–1225 of 4925 papers

TitleStatusHype
QET: Enhancing Quantized LLM Parameters and KV cache Compression through Element Substitution and Residual Clustering—0
Timestep-Aware Correction for Quantized Diffusion Models—0
OSPC: Artificial VLM Features for Hateful Meme Detection—0
Edge AI-Enabled Chicken Health Detection Based on Enhanced FCOS-Lite and Knowledge Distillation—0
Fisher-aware Quantization for DETR Detectors with Critical-category Objectives—0
Codec-ASR: Training Performant Automatic Speech Recognition Systems with Discrete Speech Representations—0
Improving Conversational Abilities of Quantized Large Language Models via Direct Preference Alignment—0
How Does Quantization Affect Multilingual LLMs?—0
GPTQT: Quantize Large Language Models Twice to Push the Efficiency—0
ADFQ-ViT: Activation-Distribution-Friendly Post-Training Quantization for Vision Transformers—0
SFC: Achieve Accurate Fast Convolution under Low-precision Arithmetic—0
Unified Anomaly Detection methods on Edge Device using Knowledge Distillation and Quantization—0
QSync: Quantization-Minimized Synchronous Distributed Training Across Hybrid DevicesCode1
PQCache: Product Quantization-based KVCache for Long Context LLM Inference—0
Linear and Nonlinear MMSE Estimation in One-Bit Quantized Systems under a Gaussian Mixture Prior—0
KV Cache Compression, But What Must We Give in Return? A Comprehensive Benchmark of Long Context Capable ApproachesCode2
Joint Pruning and Channel-wise Mixed-Precision Quantization for Efficient Deep Neural NetworksCode0
Beyond Throughput and Compression Ratios: Towards High End-to-end Utility of Gradient Compression—0
Exploring FPGA designs for MX and beyond—0
NeuroNAS: Enhancing Efficiency of Neuromorphic In-Memory Computing for Intelligent Mobile Agents through Hardware-Aware Spiking Neural Architecture Search—0
Toward a Diffusion-Based Generalist for Dense Vision Tasks—0
Rateless Stochastic Coding for Delay-Constrained Semantic Communication—0
LLMEasyQuant: Scalable Quantization for Parallel and Distributed LLM InferenceCode1
Deep Fusion Model for Brain Tumor Classification Using Fine-Grained Gradient Preservation—0
Reliable edge machine learning hardware for scientific applications—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