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 3251–3275 of 4925 papers

TitleStatusHype
Improving Robustness Against Stealthy Weight Bit-Flip Attacks by Output Code MatchingCode0
SceneSqueezer: Learning To Compress Scene for Camera Relocalization—0
Instance-Aware Dynamic Neural Network QuantizationCode0
Mutual Quantization for Cross-Modal Search With Noisy Labels—0
Quantization-Aware Deep Optics for Diffractive Snapshot Hyperspectral Imaging—0
Data-Free Network Compression via Parametric Non-Uniform Mixed Precision Quantization—0
Mr.BiQ: Post-Training Non-Uniform Quantization Based on Minimizing the Reconstruction Error—0
Croesus: Multi-Stage Processing and Transactions for Video-Analytics in Edge-Cloud Systems—0
Automatic Mixed-Precision Quantization Search of BERT—0
Studying the Interplay between Information Loss and Operation Loss in Representations for Classification—0
End-to-End Autoencoder Communications with Optimized Interference Suppression—0
Speedup deep learning models on GPU by taking advantage of efficient unstructured pruning and bit-width reduction—0
HiKonv: High Throughput Quantized Convolution With Novel Bit-wise Management and Computation—0
BMPQ: Bit-Gradient Sensitivity Driven Mixed-Precision Quantization of DNNs from Scratch—0
Stochastic Learning Equation using Monotone Increasing Resolution of Quantization—0
Distilling the Knowledge of Romanian BERTs Using Multiple TeachersCode0
Manifold learning via quantum dynamics—0
Accurate Neural Training with 4-bit Matrix Multiplications at Standard Formats—0
Nearest neighbor search with compact codes: A decoder perspective—0
Approximation of functions with one-bit neural networks—0
TAFA: Design Automation of Analog Mixed-Signal FIR Filters Using Time Approximation Architecture—0
On Recursive State Estimation for Linear State-Space Models Having Quantized Output Data—0
Boosted Dense Retriever—0
Modeling Image Quantization Tradeoffs for Optimal Compression—0
Illumination and Temperature-Aware Multispectral Networks for Edge-Computing-Enabled Pedestrian Detection—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