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 1201–1225 of 4925 papers

TitleStatusHype
AdaQAT: Adaptive Bit-Width Quantization-Aware Training—0
Collaborative Automotive Radar Sensing via Mixed-Precision Distributed Array Completion—0
Cognitive Non-Coherent Jamming Techniques for Frequency Selective Attacks—0
Self-Supervised Learning for Multi-Channel Neural Transducer—0
Dual Codebook VQ: Enhanced Image Reconstruction with Reduced Codebook Size—0
Dual Precision Quantization for Efficient and Accurate Deep Neural Networks Inference—0
E^2BoWs: An End-to-End Bag-of-Words Model via Deep Convolutional Neural Network—0
Cognitive Coding of Speech—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
A Privacy Preserving System for Movie Recommendations Using Federated Learning—0
Coding for Random Projections and Approximate Near Neighbor Search—0
AdaptivFloat: A Floating-point based Data Type for Resilient Deep Learning Inference—0
Accelerating Inference and Language Model Fusion of Recurrent Neural Network Transducers via End-to-End 4-bit Quantization—0
Coding for Random Projections—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
Codec-ASR: Training Performant Automatic Speech Recognition Systems with Discrete Speech Representations—0
A Precision-Scalable RISC-V DNN Processor with On-Device Learning Capability at the Extreme Edge—0
Adaptive Wireless Image Semantic Transmission: Design, Simulation, and Prototype Validation—0
Unsupervised automatic classification of Scanning Electron Microscopy (SEM) images of CD4+ cells with varying extent of HIV virion infection—0
CodeBrain: Impute Any Brain MRI via Instance-specific Scalar-quantized Codes—0
A Practical Mixed Precision Algorithm for Post-Training Quantization—0
Codebook based Audio Feature Representation for Music Information Retrieval—0
Codage \'echelonnable \`a granularit\'e fine de la parole : Application au codeur G.729 (Fine granularity scalable speech coding: Application to the G.729 coder) [in French]—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