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 3101–3125 of 4925 papers

TitleStatusHype
Towards Lossless ANN-SNN Conversion under Ultra-Low Latency with Dual-Phase OptimizationCode0
Real-time semantic segmentation on FPGAs for autonomous vehicles with hls4ml—0
A Comprehensive Survey on Model Quantization for Deep Neural Networks in Image Classification—0
Tighter Regret Analysis and Optimization of Online Federated Learning—0
Adaptive Block Floating-Point for Analog Deep Learning Hardware—0
Neural Network-based OFDM Receiver for Resource Constrained IoT Devices—0
Neuromimetic Linear Systems -- Resilience and Learning—0
Serving and Optimizing Machine Learning Workflows on Heterogeneous Infrastructures—0
A 14uJ/Decision Keyword Spotting Accelerator with In-SRAM-Computing and On Chip Learning for Customization—0
Protecting Data from all Parties: Combining FHE and DP in Federated Learning—0
Block Modulating Video Compression: An Ultra Low Complexity Image Compression Encoder for Resource Limited Platforms—0
Online Model Compression for Federated Learning with Large Models—0
MemSE: Fast MSE Prediction for Noisy Memristor-Based DNN Accelerators—0
Towards Feature Distribution Alignment and Diversity Enhancement for Data-Free Quantization—0
Enable Deep Learning on Mobile Devices: Methods, Systems, and Applications—0
Federated Learning with Lossy Distributed Source Coding: Analysis and Optimization—0
Improving Self-Supervised Learning-based MOS Prediction NetworksCode0
A Tale of Two Models: Constructing Evasive Attacks on Edge ModelsCode0
Arbitrary Bit-width Network: A Joint Layer-Wise Quantization and Adaptive Inference Approach—0
How to Attain Communication-Efficient DNN Training? Convert, Compress, Correct—0
Unconditional Image-Text Pair Generation with Multimodal Cross QuantizerCode0
INSTA-BNN: Binary Neural Network with INSTAnce-aware Threshold—0
Composite Code Sparse Autoencoders for first stage retrieval—0
Secure Formation Control via Edge Computing Enabled by Fully Homomorphic Encryption and Mixed Uniform-Logarithmic Quantization—0
Joint Coreset Construction and Quantization for Distributed Machine Learning—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