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 2501–2550 of 4925 papers

TitleStatusHype
Sensitivity-Aware Mixed-Precision Quantization and Width Optimization of Deep Neural Networks Through Cluster-Based Tree-Structured Parzen Estimation—0
NUPES : Non-Uniform Post-Training Quantization via Power Exponent Search—0
ReLU and Addition-based Gated RNN—0
FPGA Resource-aware Structured Pruning for Real-Time Neural Networks—0
SAfER: Layer-Level Sensitivity Assessment for Efficient and Robust Neural Network Inference—0
Vector quantization loss analysis in VQGANs: a single-GPU ablation study for image-to-image synthesisCode0
Quantization Aware Factorization for Deep Neural Network Compression—0
FLIQS: One-Shot Mixed-Precision Floating-Point and Integer Quantization Search—0
Communication-Efficient Decentralized Multi-Agent Reinforcement Learning for Cooperative Adaptive Cruise Control—0
RobustMQ: Benchmarking Robustness of Quantized Models—0
Frequency Disentangled Features in Neural Image Compression—0
Reducing Channel Estimation and Feedback Overhead in IRS-Aided Downlink System: A Quantize-then-Estimate Approach—0
Bees Local Phase Quantization Feature Selection for RGB-D Facial Expressions RecognitionCode0
Improved Knowledge Distillation for Crowd Counting on IoT DeviceCode0
Tango: rethinking quantization for graph neural network training on GPUs—0
Error Analysis of CORDIC Processor with FPGA Implementation—0
Asynchronous Federated Learning with Bidirectional Quantized Communications and Buffered Aggregation—0
AQUILA: Communication Efficient Federated Learning with Adaptive Quantization in Device Selection Strategy—0
MRQ:Support Multiple Quantization Schemes through Model Re-Quantization—0
Alternate Learning based Sparse Semantic Communications for Visual Transmission—0
An Automata-Theoretic Approach to Synthesizing Binarized Neural Networks—0
METTS: Multilingual Emotional Text-to-Speech by Cross-speaker and Cross-lingual Emotion Transfer—0
Incrementally-Computable Neural Networks: Efficient Inference for Dynamic Inputs—0
Overcoming Distribution Mismatch in Quantizing Image Super-Resolution NetworksCode0
High-Resolution Volumetric Reconstruction for Clothed Humans—0
A Model for Every User and Budget: Label-Free and Personalized Mixed-Precision QuantizationCode0
Model Compression Methods for YOLOv5: A Review—0
Communication-Efficient Federated Learning over Capacity-Limited Wireless Networks—0
Quantized Feature Distillation for Network Quantization—0
Communication-Efficient Split Learning via Adaptive Feature-Wise Compression—0
ZeroQuant-FP: A Leap Forward in LLMs Post-Training W4A8 Quantization Using Floating-Point Formats—0
Grounded Object Centric Learning—0
Extreme Image Compression using Fine-tuned VQGANs—0
Low bit rate binaural link for improved ultra low-latency low-complexity multichannel speech enhancement in Hearing Aids—0
A Survey of Techniques for Optimizing Transformer Inference—0
Self-Distilled Quantization: Achieving High Compression Rates in Transformer-Based Language Models—0
Learning Kernel-Modulated Neural Representation for Efficient Light Field Compression—0
Mixed-Precision Quantization for Deep Vision Models with Integer Quadratic ProgrammingCode0
Minimax Excess Risk of First-Order Methods for Statistical Learning with Data-Dependent Oracles—0
InfLoR-SNN: Reducing Information Loss for Spiking Neural Networks—0
Q-YOLOP: Quantization-aware You Only Look Once for Panoptic Driving Perception—0
QBitOpt: Fast and Accurate Bitwidth Reallocation during Training—0
Towards Efficient In-memory Computing Hardware for Quantized Neural Networks: State-of-the-art, Open Challenges and Perspectives—0
KP2Dtiny: Quantized Neural Keypoint Detection and Description on the EdgeCode0
ITA: An Energy-Efficient Attention and Softmax Accelerator for Quantized Transformers—0
Free Bits: Latency Optimization of Mixed-Precision Quantized Neural Networks on the Edge—0
Dequantization and Color Transfer with Diffusion ModelsCode0
Fast Private Kernel Density Estimation via Locality Sensitive QuantizationCode0
Greedy Selection for Heterogeneous Sensors—0
Data-Free Quantization via Mixed-Precision Compensation without Fine-Tuning—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