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 1826–1850 of 4925 papers

TitleStatusHype
FlatENN: Train Flat for Enhanced Fault Tolerance of Quantized Deep Neural Networks—0
FedDiSC: A Computation-efficient Federated Learning Framework for Power Systems Disturbance and Cyber Attack Discrimination—0
Flattened one-bit stochastic gradient descent: compressed distributed optimization with controlled variance—0
Fed-CVLC: Compressing Federated Learning Communications with Variable-Length Codes—0
Compensate Quantization Errors: Make Weights Hierarchical to Compensate Each Other—0
A review of learning vector quantization classifiers—0
A Different View of Sigma-Delta Modulators Under the Lens of Pulse Frequency Modulation—0
Flexible Unsupervised Learning for Massive MIMO Subarray Hybrid Beamforming—0
FleXOR: Trainable Fractional Quantization—0
3DQ: Compact Quantized Neural Networks for Volumetric Whole Brain Segmentation—0
FedComLoc: Communication-Efficient Distributed Training of Sparse and Quantized Models—0
FlightLLM: Efficient Large Language Model Inference with a Complete Mapping Flow on FPGAs—0
FLightNNs: Lightweight Quantized Deep Neural Networks for Fast and Accurate Inference—0
FedAQ: Communication-Efficient Federated Edge Learning via Joint Uplink and Downlink Adaptive Quantization—0
Comparison of 14 different families of classification algorithms on 115 binary datasets—0
Feature Quantization for Defending Against Distortion of Images—0
FlowPrecision: Advancing FPGA-Based Real-Time Fluid Flow Estimation with Linear Quantization—0
Comparing Iterative and Least-Squares Based Phase Noise Tracking in Receivers with 1-bit Quantization and Oversampling—0
FlowVQTalker: High-Quality Emotional Talking Face Generation through Normalizing Flow and Quantization—0
High-performance deep spiking neural networks with 0.3 spikes per neuron—0
FoldToken2: Learning compact, invariant and generative protein structure language—0
Comparing Fisher Information Regularization with Distillation for DNN Quantization—0
Foothill: A Quasiconvex Regularization for Edge Computing of Deep Neural Networks—0
Forearm Ultrasound based Gesture Recognition on Edge—0
Feature Affinity Assisted Knowledge Distillation and Quantization of Deep Neural Networks on Label-Free Data—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