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 3126–3150 of 4925 papers

TitleStatusHype
HW-TSC’s Participation in the WMT 2021 Efficiency Shared Task—0
Efficient Machine Translation with Model Pruning and Quantization—0
Matching-oriented Embedding Quantization For Ad-hoc RetrievalCode1
Simple and Effective Unsupervised Redundancy Elimination to Compress Dense Vectors for Passage Retrieval—0
Block-wise Word Embedding Compression Revisited: Better Weighting and StructuringCode1
Arch-Net: Model Distillation for Architecture Agnostic Model DeploymentCode1
Structure Information is the Key: Self-Attention RoI Feature Extractor in 3D Object Detection—0
PP-ShiTu: A Practical Lightweight Image Recognition SystemCode0
Reconfigurable Intelligent Surface-induced Randomness for mmWave Key Generation—0
DAdaQuant: Doubly-adaptive quantization for communication-efficient Federated Learning—0
Revealing and Protecting Labels in Distributed TrainingCode0
RMSMP: A Novel Deep Neural Network Quantization Framework with Row-wise Mixed Schemes and Multiple Precisions—0
ILMPQ : An Intra-Layer Multi-Precision Deep Neural Network Quantization framework for FPGA—0
Nash equilibrium of multi-agent graphical game with a privacy information encrypted learning algorithm—0
FAST: DNN Training Under Variable Precision Block Floating Point with Stochastic Rounding—0
MERCURY: Accelerating DNN Training By Exploiting Input Similarity—0
VQ-GNN: A Universal Framework to Scale up Graph Neural Networks using Vector QuantizationCode1
Differential Deep Detection in Massive MIMO With One-Bit ADC—0
TOD: GPU-accelerated Outlier Detection via Tensor OperationsCode1
High-Order Signed Distance Transform of Sampled Signals—0
Qu-ANTI-zation: Exploiting Quantization Artifacts for Achieving Adversarial OutcomesCode1
Demystifying and Generalizing BinaryConnect—0
Algorithms for the Communication of Samples—0
Convolutional Autoencoder-Based Phase Shift Feedback Compression for Intelligent Reflecting Surface-Assisted Wireless SystemsCode1
Task-Based Graph Signal CompressionCode0
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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