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 1651–1700 of 4925 papers

TitleStatusHype
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
Explicit Loss-Error-Aware Quantization for Low-Bit Deep Neural Networks—0
Exploiting Change Blindness for Video Coding: Perspectives from a Less Promising User Study—0
Exploiting Intelligent Reflecting Surfaces in NOMA Networks: Joint Beamforming Optimization—0
Exploiting Latent Properties to Optimize Neural Codecs—0
Optimizing Learned Image Compression on Scalar and Entropy-Constraint Quantization—0
Exploiting Modern Hardware for High-Dimensional Nearest Neighbor Search—0
Exploiting Non-uniform Quantization for Enhanced ILC in Wideband Digital Pre-distortion—0
Exploiting Offset-guided Network for Pose Estimation and Tracking—0
Cognitive Coding of Speech—0
A Probabilistic Reformulation Technique for Discrete RIS Optimization in Wireless Systems—0
Dynamic Stashing Quantization for Efficient Transformer Training—0
Exploration of Activation Fault Reliability in Quantized Systolic Array-Based DNN Accelerators—0
Explore Cross-Codec Quality-Rate Convex Hulls Relation for Adaptive Streaming—0
Explore the Potential of CNN Low Bit Training—0
Exploring Automatic Gym Workouts Recognition Locally On Wearable Resource-Constrained Devices—0
Collaborative Edge AI Inference over Cloud-RAN—0
Exploring Extreme Quantization in Spiking Language Models—0
Exploring FPGA designs for MX and beyond—0
Exploring Model Invariance with Discrete Search for Ultra-Low-Bit Quantization—0
Exploring Neural Networks Quantization via Layer-Wise Quantization Analysis—0
Dynamic Signal Measurements Based on Quantized Data—0
Collaborative Quantization Embeddings for Intra-Subject Prostate MR Image Registration—0
Dynamic quantized consensus under DoS attacks: Towards a tight zooming-out factor—0
Dynamic Quantized Consensus of General Linear Multi-agent Systems under Denial-of-Service Attacks—0
Exploring Semantic Segmentation on the DCT Representation—0
Blind-Adaptive Quantizers—0
An Extra RMSNorm is All You Need for Fine Tuning to 1.58 Bits—0
Bracket Diffusion: HDR Image Generation by Consistent LDR Denoising—0
Post Training Quantization of Large Language Models with Microscaling Formats—0
Extreme Compression for Pre-trained Transformers Made Simple and Efficient—0
Dynamic Q&A of Clinical Documents with Large Language Models—0
Extreme Image Compression using Fine-tuned VQGANs—0
Dynamic Probabilistic Pruning: A general framework for hardware-constrained pruning at different granularities—0
Extremely Low Bit Neural Network: Squeeze the Last Bit Out with ADMM—0
Blending Low and High-Level Semantics of Time Series for Better Masked Time Series Generation—0
Dynamic Predictive Sampling Analog to Digital Converter for Sparse Signal Sensing—0
Face recognition using color local binary pattern from mutually independent color channels—0
Factorized Visual Tokenization and Generation—0
Blended Coarse Gradient Descent for Full Quantization of Deep Neural Networks—0
FactorizeNet: Progressive Depth Factorization for Efficient Network Architecture Exploration Under Quantization Constraints—0
Communication-Efficient Decentralized Multi-Agent Reinforcement Learning for Cooperative Adaptive Cruise Control—0
False Detection (Positives and Negatives) in Object Detection—0
ADaPTION: Toolbox and Benchmark for Training Convolutional Neural Networks with Reduced Numerical Precision Weights and Activation—0
FantastIC4: A Hardware-Software Co-Design Approach for Efficiently Running 4bit-Compact Multilayer Perceptrons—0
FAQ: Mitigating the Impact of Faults in the Weight Memory of DNN Accelerators through Fault-Aware Quantization—0
FAQS: Communication-efficient Federate DNN Architecture and Quantization Co-Search for personalized Hardware-aware Preferences—0
Fixed Point Quantization of Deep Convolutional Networks—0
FlashAttention on a Napkin: A Diagrammatic Approach to Deep Learning IO-Awareness—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