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

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Showing 2751–2800 of 4925 papers

TitleStatusHype
Delving into Channels: Exploring Hyperparameter Space of Channel Bit Widths with Linear Complexity—0
Demystifying and Generalizing BinaryConnect—0
Demystifying Singular Defects in Large Language Models—0
Conditional Denoising Diffusion Probabilistic Models for Data Reconstruction Enhancement in Wireless Communications—0
Deploying Large AI Models on Resource-Limited Devices with Split Federated Learning—0
Deploy Large-Scale Deep Neural Networks in Resource Constrained IoT Devices with Local Quantization Region—0
Deployment of Deep Neural Networks for Object Detection on Edge AI Devices with Runtime Optimization—0
Energy-efficient Deployment of Deep Learning Applications on Cortex-M based Microcontrollers using Deep Compression—0
Dequantization of a signal from two parallel quantized observations—0
Derived Codebooks for High-Accuracy Nearest Neighbor Search—0
DeRS: Towards Extremely Efficient Upcycled Mixture-of-Experts Models—0
Design and Analysis of Hardware-limited Non-uniform Task-based Quantizers—0
Design and Analysis of Uplink and Downlink Communications for Federated Learning—0
Design Automation for Efficient Deep Learning Computing—0
Design Flow of Accelerating Hybrid Extremely Low Bit-width Neural Network in Embedded FPGA—0
Designing a Classifier for Active Fire Detection from Multispectral Satellite Imagery Using Neural Architecture Search—0
Designing Discontinuities—0
Designing DNNs for a trade-off between robustness and processing performance in embedded devices—0
Designing strong baselines for ternary neural network quantization through support and mass equalization—0
Design of High-Throughput Mixed-Precision CNN Accelerators on FPGA—0
Design of Sampling Set for Bandlimited Graph Signal Estimation—0
Design of Stochastic Quantizers for Privacy Preservation—0
Design Space Exploration of Dense and Sparse Mapping Schemes for RRAM Architectures—0
Design Space Exploration of Low-Bit Quantized Neural Networks for Visual Place Recognition—0
Detecting Dead Weights and Units in Neural Networks—0
Detecting Face Synthesis Using a Concealed Fusion Model—0
DHNet: Double MPEG-4 Compression Detection via Multiple DCT Histograms—0
Detection of small changes in medical and random-dot images comparing self-organizing map performance to human detection—0
Development of a Thermodynamics of Human Cognition and Human Culture—0
Development of Quantized DNN Library for Exact Hardware Emulation—0
Device Interoperability for Learned Image Compression with Weights and Activations Quantization—0
DFTerNet: Towards 2-bit Dynamic Fusion Networks for Accurate Human Activity Recognition—0
Diagnostic data integration using deep neural networks for real-time plasma analysis—0
Differentiable Discrete Device-to-System Codesign for Optical Neural Networks via Gumbel-Softmax—0
Differentiable Dynamic Quantization with Mixed Precision and Adaptive Resolution—0
Differentiable Joint Pruning and Quantization for Hardware Efficiency—0
Differentiable Product Quantization for Learning Compact Embedding Layers—0
Differentiable Search for Finding Optimal Quantization Strategy—0
Differentiable Training for Hardware Efficient LightNNs—0
Differential Deep Detection in Massive MIMO With One-Bit ADC—0
Differential error feedback for communication-efficient decentralized learning—0
Differential Modulation in Massive MIMO With Low-Resolution ADCs—0
Differential Privacy with Random Projections and Sign Random Projections—0
Diffusion-based Perceptual Neural Video Compression with Temporal Diffusion Information Reuse—0
Diffusion Product Quantization—0
DilateQuant: Accurate and Efficient Diffusion Quantization via Weight Dilation—0
DILEMMA: Joint LLM Quantization and Distributed LLM Inference Over Edge Computing Systems—0
Dimensionality-Dependent Generalization Bounds for k-Dimensional Coding Schemes—0
Dimension-Free Bounds for Low-Precision Training—0
DipSVD: Dual-importance Protected SVD for Efficient LLM Compression—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