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 3551–3575 of 4925 papers

TitleStatusHype
A Low Memory Footprint Quantized Neural Network for Depth Completion of Very Sparse Time-of-Flight Depth Maps—0
AlphaTuning: Quantization-Aware Parameter-Efficient Adaptation of Large-Scale Pre-Trained Language Models—0
Alternate Learning based Sparse Semantic Communications for Visual Transmission—0
Alternating Co-Quantization for Cross-Modal Hashing—0
Alternating Direction Method of Multipliers for Quantization—0
Alternating Multi-bit Quantization for Recurrent Neural Networks—0
A Markovian Model-Driven Deep Learning Framework for Massive MIMO CSI Feedback—0
A Memory-Efficient Learning Framework for SymbolLevel Precoding with Quantized NN Weights—0
Amenable Sparse Network Investigator—0
A method of using RSVD in residual calculation of LowBit GEMM—0
A Modular Neural Network Based Deep Learning Approach for MIMO Signal Detection—0
A multi-layer image representation using Regularized Residual Quantization: application to compression and denoising—0
AMXFP4: Taming Activation Outliers with Asymmetric Microscaling Floating-Point for 4-bit LLM Inference—0
An Adaptive Device-Edge Co-Inference Framework Based on Soft Actor-Critic—0
An adaptive random experiment design method for engineering experiment—0
An Adaptive Statistical Non-uniform Quantizer for Detail Wavelet Components in Lossy JPEG2000 Image Compression—0
An Additive Latent Feature Model for Transparent Object Recognition—0
Analog-digital Scheduling for Federated Learning: A Communication-Efficient Approach—0
Analog Spiking Neuron in CMOS 28 nm Towards Large-Scale Neuromorphic Processors—0
Analysis and Optimization for RIS-Aided Multi-Pair Communications Relying on Statistical CSI—0
Analysis of Null Related Beampattern Measures and Signal Quantization Effects for Linear Differential Microphone Arrays—0
Analysis of Oversampling in Uplink Massive MIMO-OFDM with Low-Resolution ADCs—0
Analysis of Quantization on MLP-based Vision Models—0
Analysis of Quantized Models—0
Analysis of the influence of final resolution on ADC accuracy—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