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 4101–4150 of 4925 papers

TitleStatusHype
Query-Adaptive Hash Code Ranking for Large-Scale Multi-View Visual Search—0
QUIC-FL: Quick Unbiased Compression for Federated Learning—0
QuickSRNet: Plain Single-Image Super-Resolution Architecture for Faster Inference on Mobile Platforms—0
QUIDAM: A Framework for Quantization-Aware DNN Accelerator and Model Co-Exploration—0
QuPeD: Quantized Personalization via Distillation with Applications to Federated Learning—0
QuPeL: Quantized Personalization with Applications to Federated Learning—0
QVD: Post-training Quantization for Video Diffusion Models—0
Qwen2.5-32B: Leveraging Self-Consistent Tool-Integrated Reasoning for Bengali Mathematical Olympiad Problem Solving—0
Q-YOLO: Efficient Inference for Real-time Object Detection—0
Q-YOLOP: Quantization-aware You Only Look Once for Panoptic Driving Perception—0
R2 Loss: Range Restriction Loss for Model Compression and Quantization—0
Radio: Rate-Distortion Optimization for Large Language Model Compression—0
RAG-based User Profiling for Precision Planning in Mixed-precision Over-the-Air Federated Learning—0
Random Binary Mappings for Kernel Learning and Efficient SVM—0
Random Projections with Asymmetric Quantization—0
Random VLAD based Deep Hashing for Efficient Image Retrieval—0
Random Walk Graph Laplacian based Smoothness Prior for Soft Decoding of JPEG Images—0
RAND: Robustness Aware Norm Decay For Quantized Seq2seq Models—0
Rapid Deployment of Domain-specific Hyperspectral Image Processors with Application to Autonomous Driving—0
Rapid yet accurate Tile-circuit and device modeling for Analog In-Memory Computing—0
Rate-aware Compression for NeRF-based Volumetric Video—0
Rate-Constrained Quantization for Communication-Efficient Federated Learning—0
Rate-Distortion-Cognition Controllable Versatile Neural Image Compression—0
Rate distortion comparison of a few gradient quantizers—0
Rate Distortion For Model Compression: From Theory To Practice—0
Rate-Distortion Optimization with Non-Reference Metrics for UGC Compression—0
Rate-Distortion Optimized Post-Training Quantization for Learned Image Compression—0
Rateless Stochastic Coding for Delay-Constrained Semantic Communication—0
Rate-Loss Mitigation for a Millimeter-Wave Beamspace MIMO Lens Antenna Array System Using a Hybrid Beam-Selection Scheme—0
Rate-splitting Multiple Access for Hierarchical HAP-LAP Networks under Limited Fronthaul—0
RBNN: Memory-Efficient Reconfigurable Deep Binary Neural Network with IP Protection for Internet of Things—0
RCNet: Incorporating Structural Information into Deep RNN for MIMO-OFDM Symbol Detection with Limited Training—0
Poisoning Semi-supervised Federated Learning via Unlabeled Data: Attacks and Defenses—0
Lightweight Embedded FPGA Deployment of Learned Image Compression with Knowledge Distillation and Hybrid Quantization—0
ReactDance: Progressive-Granular Representation for Long-Term Coherent Reactive Dance Generation—0
Experimental implementation of a neural network optical channel equalizer in restricted hardware using pruning and quantization—0
Realizing a Low-Power Head-Mounted Phase-Only Holographic Display by Light-Weight Compression—0
ReALLM: A general framework for LLM compression and fine-tuning—0
Real-Time detection, classification and DOA estimation of Unmanned Aerial Vehicle—0
Real-Time Distributed Model Predictive Control with Limited Communication Data Rates—0
Real-time Hyper-Dimensional Reconfiguration at the Edge using Hardware Accelerators—0
Real-time Indexing for Large-scale Recommendation by Streaming Vector Quantization Retriever—0
Real-time Mask Detection on Google Edge TPU—0
Real-Time Object Detection and Recognition on Low-Compute Humanoid Robots using Deep Learning—0
Real-time semantic segmentation on FPGAs for autonomous vehicles with hls4ml—0
Real-Time Stress Detection via Photoplethysmogram Signals: Implementation of a Combined Continuous Wavelet Transform and Convolutional Neural Network on Resource-Constrained Microcontrollers—0
RecDis-SNN: Rectifying Membrane Potential Distribution for Directly Training Spiking Neural Networks—0
Received Power Maximization Using Nonuniform Discrete Phase Shifts for RISs With a Limited Phase Range—0
Recent Advances in Efficient Computation of Deep Convolutional Neural Networks—0
Recommender systems inspired by the structure of quantum theory—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