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 3501–3525 of 4925 papers

TitleStatusHype
Zeroth-Order Fine-Tuning of LLMs with Extreme Sparsity—0
ZipML: Training Linear Models with End-to-End Low Precision, and a Little Bit of Deep Learning—0
ZipVL: Efficient Large Vision-Language Models with Dynamic Token Sparsification—0
ZOBNN: Zero-Overhead Dependable Design of Binary Neural Networks with Deliberately Quantized Parameters—0
1.58-bit FLUX—0
MobiVSR: A Visual Speech Recognition Solution for Mobile Devices—0
Model Agnostic Hybrid Sharding For Heterogeneous Distributed Inference—0
Model-Based Detector for SSDs in the Presence of Inter-cell Interference—0
Model Compression—0
Model Compression and Efficient Inference for Large Language Models: A Survey—0
Model compression as constrained optimization, with application to neural nets. Part II: quantization—0
Model compression as constrained optimization, with application to neural nets. Part I: general framework—0
Model compression as constrained optimization, with application to neural nets. Part V: combining compressions—0
Model Compression for DNN-based Speaker Verification Using Weight Quantization—0
Model Compression Methods for YOLOv5: A Review—0
Model Hemorrhage and the Robustness Limits of Large Language Models—0
Modeling Image Quantization Tradeoffs for Optimal Compression—0
Modeling Realistic Degradations in Non-blind Deconvolution—0
Model Predictive Control for Neuromimetic Quantized Systems—0
Model Selection CNN-based VVC QualityEnhancement—0
Modular Transformers: Compressing Transformers into Modularized Layers for Flexible Efficient Inference—0
Modulation For Modulo: A Sampling-Efficient High-Dynamic Range ADC—0
Modulo Sampling: Performance Guarantees in The Presence of Quantization—0
MoGenTS: Motion Generation based on Spatial-Temporal Joint Modeling—0
Mokey: Enabling Narrow Fixed-Point Inference for Out-of-the-Box Floating-Point Transformer Models—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