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 1701–1725 of 4925 papers

TitleStatusHype
LRP-QViT: Mixed-Precision Vision Transformer Quantization via Layer-wise Relevance Propagation—0
Dynamic Q&A of Clinical Documents with Large Language Models—0
A2Q+: Improving Accumulator-Aware Weight QuantizationCode0
Enabling On-device Continual Learning with Binary Neural Networks—0
Model Compression Techniques in Biometrics Applications: A SurveyCode0
Exploration of Activation Fault Reliability in Quantized Systolic Array-Based DNN Accelerators—0
Hybrid of DiffStride and Spectral Pooling in Convolutional Neural Networks—0
Inferflow: an Efficient and Highly Configurable Inference Engine for Large Language ModelsCode3
Hardware Acceleration for Real-Time Wildfire Detection Onboard Drone NetworksCode0
TP-Aware Dequantization—0
Activations and Gradients Compression for Model-Parallel TrainingCode0
MorpheusNet: Resource efficient sleep stage classifier for embedded on-line systemsCode0
HiHPQ: Hierarchical Hyperbolic Product Quantization for Unsupervised Image RetrievalCode1
ENTED: Enhanced Neural Texture Extraction and Distribution for Reference-based Blind Face Restoration—0
Extreme Compression of Large Language Models via Additive QuantizationCode5
Correlated Quantization for Faster Nonconvex Distributed Optimization—0
RoSA: Accurate Parameter-Efficient Fine-Tuning via Robust AdaptationCode3
EDA-DM: Enhanced Distribution Alignment for Post-Training Quantization of Diffusion ModelsCode1
Memory-Efficient Fine-Tuning for Quantized Diffusion Model—0
Detecting Face Synthesis Using a Concealed Fusion Model—0
A Video Coding Method Based on Neural Network for CLIC2024—0
FlightLLM: Efficient Large Language Model Inference with a Complete Mapping Flow on FPGAs—0
Data-driven Dynamic Event-triggered Control—0
A Cost-Efficient FPGA Implementation of Tiny Transformer Model using Neural ODE—0
Enhancing Generalization of Invisible Facial Privacy Cloak via Gradient Accumulation—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