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 2351–2375 of 4925 papers

TitleStatusHype
A White Paper on Neural Network Quantization—0
Demystifying Singular Defects in Large Language Models—0
Joint Neural Architecture Search and Quantization—0
Joint Optimization of Rate, Distortion, and Decoding Energy for HEVC Intraframe Coding—0
AlphaTuning: Quantization-Aware Parameter-Efficient Adaptation of Large-Scale Pre-Trained Language Models—0
Bang for the Buck: Vector Search on Cloud CPUs—0
Learning-Based Latency-Constrained Fronthaul Compression Optimization in C-RAN—0
Joint Quantization and Pruning Neural Networks Approach: A Case Study on FSO Receivers—0
Joint Sequential Fronthaul Quantization and Hardware Complexity Reduction in Uplink Cell-Free Massive MIMO Networks—0
Joint SPX-VIX calibration with Gaussian polynomial volatility models: deep pricing with quantization hints—0
Intelligent Fault Diagnosis of Type and Severity in Low-Frequency, Low Bit-Depth Signals—0
Joint Texture and Geometry Optimization for RGB-D Reconstruction—0
Joshua 4.0: Packing, PRO, and Paraphrases—0
Deployment of Deep Neural Networks for Object Detection on Edge AI Devices with Runtime Optimization—0
JPEG-LM: LLMs as Image Generators with Canonical Codec Representations—0
JPEG Quantized Coefficient Recovery via DCT Domain Spatial-Frequential Transformer—0
Integrating PHY Security Into NDN-IoT Networks By Exploiting MEC: Authentication Efficiency, Robustness, and Accuracy Enhancement—0
Deep neural networks are robust to weight binarization and other non-linear distortions—0
Dequantization of a signal from two parallel quantized observations—0
AWEQ: Post-Training Quantization with Activation-Weight Equalization for Large Language Models—0
Deep neural networks algorithms for stochastic control problems on finite horizon: convergence analysis—0
KDLSQ-BERT: A Quantized Bert Combining Knowledge Distillation with Learned Step Size Quantization—0
Kernel k-Medoids as General Vector Quantization—0
Kernel Quantization for Efficient Network Compression—0
Learning a Virtual Codec Based on Deep Convolutional Neural Network to Compress Image—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