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 3076–3100 of 4925 papers

TitleStatusHype
Towards Energy-Efficient and Secure Edge AI: A Cross-Layer Framework—0
Towards Energy Efficient Federated Learning over 5G+ Mobile Devices—0
HYPER-SNN: Towards Energy-efficient Quantized Deep Spiking Neural Networks for Hyperspectral Image Classification—0
Towards Fast and Accurate Neural Chinese Word Segmentation with Multi-Criteria Learning—0
Towards Fully 8-bit Integer Inference for the Transformer Model—0
Towards Hardware-Specific Automatic Compression of Neural Networks—0
Towards Improved Text-Aligned Codebook Learning: Multi-Hierarchical Codebook-Text Alignment with Long Text—0
Towards Intelligent Millimeter and Terahertz Communication for 6G: Computer Vision-aided Beamforming—0
Towards Learning Discrete Representations via Self-Supervision for Wearables-Based Human Activity Recognition—0
Towards Lightweight Speaker Verification via Adaptive Neural Network Quantization—0
Towards Low-bit Communication for Tensor Parallel LLM Inference—0
Exploring the Potential of Low-bit Training of Convolutional Neural Networks—0
Towards Low-loss 1-bit Quantization of User-item Representations for Top-K Recommendation—0
Towards Mixed-Precision Quantization of Neural Networks via Constrained Optimization—0
Model-Free Learning for the Linear Quadratic Regulator over Rate-Limited Channels—0
Towards Neural Variational Monte Carlo That Scales Linearly with System Size—0
Towards Next-Level Post-Training Quantization of Hyper-Scale Transformers—0
Towards On-Device Face Recognition in Body-worn Cameras—0
Towards Optimal Compression: Joint Pruning and Quantization—0
Towards Practical and Efficient Image-to-Speech Captioning with Vision-Language Pre-training and Multi-modal Tokens—0
Towards Practical Single-shot Phase Retrieval with Physics-Driven Deep Neural Network—0
Towards Real-Time Neural Video Codec for Cross-Platform Application Using Calibration Information—0
Towards Real-Time Neural Volumetric Rendering on Mobile Devices: A Measurement Study—0
Towards Reasoning Ability of Small Language Models—0
Towards Robust Low Light Image Enhancement—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