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 2901–2925 of 4925 papers

TitleStatusHype
Accelerating RNN-based Speech Enhancement on a Multi-Core MCU with Mixed FP16-INT8 Post-Training Quantization—0
SNN-SC: A Spiking Semantic Communication Framework for Collaborative IntelligenceCode0
SQuAT: Sharpness- and Quantization-Aware Training for BERT—0
ScionFL: Efficient and Robust Secure Quantized Aggregation—0
Statistical Modeling of Soft Error Influence on Neural Networks—0
Deep learning model compression using network sensitivity and gradients—0
Super-Resolution by Predicting Offsets: An Ultra-Efficient Super-Resolution Network for Rasterized Images—0
AlphaTuning: Quantization-Aware Parameter-Efficient Adaptation of Large-Scale Pre-Trained Language Models—0
Learning to embed semantic similarity for joint image-text retrieval—0
IR2Net: Information Restriction and Information Recovery for Accurate Binary Neural NetworksCode0
Self-triggered Consensus of Multi-agent Systems with Quantized Relative State Measurements—0
Sampling Streaming Data with Parallel Vector Quantization -- PVQ—0
A Novel Light Field Coding Scheme Based on Deep Belief Network & Weighted Binary Images for Additive Layered Displays—0
Convolutional Neural Networks Quantization with Attention—0
Goal-Oriented Quantization: Analysis, Design, and Application to Resource Allocation—0
A Secure Federated Learning Framework for Residential Short Term Load Forecasting—0
Speech Enhancement Using Self-Supervised Pre-Trained Model and Vector Quantization—0
Physics-aware Differentiable Discrete Codesign for Diffractive Optical Neural Networks—0
Multi-Sample Training for Neural Image Compression—0
The Cramer-Rao Bound for Signal Parameter Estimation from Quantized Data—0
Device-friendly Guava fruit and leaf disease detection using deep learningCode0
Going Further With Winograd Convolutions: Tap-Wise Quantization for Efficient Inference on 4x4 Tile—0
Lightweight Image Codec via Multi-Grid Multi-Block-Size Vector Quantization (MGBVQ)—0
SpeedLimit: Neural Architecture Search for Quantized Transformer Models—0
Vector Quantized Semantic Communication System—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