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 2751–2775 of 4925 papers

TitleStatusHype
BL-JUNIPER: A CNN-Assisted Framework for Perceptual Video Coding Leveraging Block-Level JNDCode1
Supervised Deep Hashing for High-dimensional and Heterogeneous Case-based Reasoning—0
Multi-user Downlink Beamforming using Uplink Downlink Duality with 1-bit Converters for Flat Fading Channels—0
Fundamental Limits of Communication Efficiency for Model Aggregation in Distributed Learning: A Rate-Distortion Approach—0
QTI Submission to DCASE 2021: residual normalization for device-imbalanced acoustic scene classification with efficient design—0
CTMQ: Cyclic Training of Convolutional Neural Networks with Multiple Quantization Steps—0
Cactus Mechanisms: Optimal Differential Privacy Mechanisms in the Large-Composition Regime—0
Inverted Semantic-Index for Image Retrieval—0
Computational Complexity Evaluation of Neural Network Applications in Signal Processing—0
Megapixel Image Generation with Step-Unrolled Denoising AutoencodersCode0
QReg: On Regularization Effects of Quantization—0
Open-source FPGA-ML codesign for the MLPerf Tiny BenchmarkCode0
A Federated Reinforcement Learning Method with Quantization for Cooperative Edge Caching in Fog Radio Access Networks—0
Automated Cancer Subtyping via Vector Quantization Mutual Information MaximizationCode0
Proximity Graph Maintenance for Fast Online Nearest Neighbor Search—0
Quantization Robust Federated Learning for Efficient Inference on Heterogeneous Devices—0
Asymmetric Learned Image Compression with Multi-Scale Residual Block, Importance Map, and Post-Quantization Filtering—0
QuantFace: Towards Lightweight Face Recognition by Synthetic Data Low-bit QuantizationCode1
sqSGD: Locally Private and Communication Efficient Federated Learning—0
Self-Supervised Consistent Quantization for Fully Unsupervised Image Retrieval—0
Low-Precision Stochastic Gradient Langevin DynamicsCode0
LUT-GEMM: Quantized Matrix Multiplication based on LUTs for Efficient Inference in Large-Scale Generative Language ModelsCode1
Seizure Detection and Prediction by Parallel Memristive Convolutional Neural NetworksCode1
Towards Efficient Active Learning of PDFACode0
FedNew: A Communication-Efficient and Privacy-Preserving Newton-Type Method for Federated LearningCode1
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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