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 2101–2125 of 4925 papers

TitleStatusHype
Exploration of Activation Fault Reliability in Quantized Systolic Array-Based DNN Accelerators—0
Exploiting Weight Redundancy in CNNs: Beyond Pruning and Quantization—0
Collaborative Automotive Radar Sensing via Mixed-Precision Distributed Array Completion—0
Cognitive Non-Coherent Jamming Techniques for Frequency Selective Attacks—0
AdaQAT: Adaptive Bit-Width Quantization-Aware Training—0
Accelerating Neural Network Inference by Overflow Aware Quantization—0
3D Gaussian Splatting Data Compression with Mixture of Priors—0
Self-Supervised Learning for Multi-Channel Neural Transducer—0
Cognitive Coding of Speech—0
Exploiting Offset-guided Network for Pose Estimation and Tracking—0
Exploiting Non-uniform Quantization for Enhanced ILC in Wideband Digital Pre-distortion—0
CogACT: A Foundational Vision-Language-Action Model for Synergizing Cognition and Action in Robotic Manipulation—0
A Probabilistic Reformulation Technique for Discrete RIS Optimization in Wireless Systems—0
Exploiting Modern Hardware for High-Dimensional Nearest Neighbor Search—0
Exploiting Latent Properties to Optimize Neural Codecs—0
Exploiting Intelligent Reflecting Surfaces in NOMA Networks: Joint Beamforming Optimization—0
Coding for Random Projections and Approximate Near Neighbor Search—0
A Privacy Preserving System for Movie Recommendations Using Federated Learning—0
AdaptivFloat: A Floating-point based Data Type for Resilient Deep Learning Inference—0
Exploiting Change Blindness for Video Coding: Perspectives from a Less Promising User Study—0
Explicit Loss-Error-Aware Quantization for Low-Bit Deep Neural Networks—0
Coding for Random Projections—0
Experimental results on palmvein-based personal recognition by multi-snapshot fusion of textural features—0
Co-Designing Binarized Transformer and Hardware Accelerator for Efficient End-to-End Edge Deployment—0
A Principled Hierarchical Deep Learning Approach to Joint Image Compression and Classification—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