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 1151–1175 of 4925 papers

TitleStatusHype
DNQ: Dynamic Network Quantization—0
Communication-efficient Variance-reduced Stochastic Gradient Descent—0
Communication-Efficient Split Learning via Adaptive Feature-Wise Compression—0
Communication Efficient SGD via Gradient Sampling With Bayes Prior—0
AddNet: Deep Neural Networks Using FPGA-Optimized Multipliers—0
Dithered backprop: A sparse and quantized backpropagation algorithm for more efficient deep neural network training—0
Arbitrary Bit-width Network: A Joint Layer-Wise Quantization and Adaptive Inference Approach—0
Communication-efficient k-Means for Edge-based Machine Learning—0
Acceleration for Compressed Gradient Descent in Distributed Optimization—0
Communication-Efficient Federated Learning by Quantized Variance Reduction for Heterogeneous Wireless Edge Networks—0
Arabic Compact Language Modelling for Resource Limited Devices—0
Additive Quantization for Extreme Vector Compression—0
Ditto: Accelerating Diffusion Model via Temporal Value Similarity—0
Communication-Efficient Federated Learning over Capacity-Limited Wireless Networks—0
Communication-Efficient Federated Learning via Quantized Compressed Sensing—0
AQUILA: Communication Efficient Federated Learning with Adaptive Quantization in Device Selection Strategy—0
Communication-Efficient Federated Learning via Optimal Client Sampling—0
Communication Efficient Federated Learning over Multiple Access Channels—0
Quantum Block-Matching Algorithm using Dissimilarity Measure—0
3DMolNet: A Generative Network for Molecular Structures—0
Communication-Efficient Federated Distillation—0
Reducing Channel Estimation and Feedback Overhead in IRS-Aided Downlink System: A Quantize-then-Estimate Approach—0
Communication Efficient Distributed Learning with Censored, Quantized, and Generalized Group ADMM—0
Accelerating RNN-based Speech Enhancement on a Multi-Core MCU with Mixed FP16-INT8 Post-Training Quantization—0
Distribution-Aware Adaptive Multi-Bit Quantization—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