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 1201–1250 of 4925 papers

TitleStatusHype
Collaborative Filtering with Smooth Reconstruction of the Preference Function—0
Collaborative Edge AI Inference over Cloud-RAN—0
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
EDAS: Efficient and Differentiable Architecture Search—0
Edge AI-Enabled Chicken Health Detection Based on Enhanced FCOS-Lite and Knowledge Distillation—0
Edge Intelligence Optimization for Large Language Model Inference with Batching and Quantization—0
Effective and Fast: A Novel Sequential Single Path Search for Mixed-Precision Quantization—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
Adaptive Wireless Image Semantic Transmission and Over-The-Air Testing—0
Cocktail: Chunk-Adaptive Mixed-Precision Quantization for Long-Context LLM Inference—0
Approximation speed of quantized vs. unquantized ReLU neural networks and beyond—0
CoAst: Validation-Free Contribution Assessment for Federated Learning based on Cross-Round Valuation—0
Approximation of functions with one-bit neural networks—0
Adaptive Transmission for Distributed Detection in Energy Harvesting Wireless Sensor Networks—0
2-in-1 Accelerator: Enabling Random Precision Switch for Winning Both Adversarial Robustness and Efficiency—0
E^2BoWs: An End-to-End Bag-of-Words Model via Deep Convolutional Neural Network—0
COAP: Memory-Efficient Training with Correlation-Aware Gradient Projection—0
CNN inference acceleration using dictionary of centroids—0
Accelerating Energy-Efficient Federated Learning in Cell-Free Networks with Adaptive Quantization—0
CNN-Based Equalization for Communications: Achieving Gigabit Throughput with a Flexible FPGA Hardware Architecture—0
Adaptive Training of Random Mapping for Data Quantization—0
Reconstruction of Privacy-Sensitive Data from Protected Templates—0
CNN-based Analog CSI Feedback in FDD MIMO-OFDM Systems—0
CNN Acceleration by Low-rank Approximation with Quantized Factors—0
Approximate search with quantized sparse representations—0
CNN2Gate: Toward Designing a General Framework for Implementation of Convolutional Neural Networks on FPGA—0
Cluster Regularized Quantization for Deep Networks Compression—0
Approximate Probabilistic Neural Networks with Gated Threshold Logic—0
Adaptive Sample-space & Adaptive Probability coding: a neural-network based approach for compression—0
DynaQuant: Compressing Deep Learning Training Checkpoints via Dynamic Quantization—0
Show:102550
← PrevPage 25 of 99Next →
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