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 2301–2325 of 4925 papers

TitleStatusHype
Adaptive Precision Training: Quantify Back Propagation in Neural Networks with Fixed-point Numbers—0
Challenging GPU Dominance: When CPUs Outperform for On-Device LLM Inference—0
Empirical Evaluation of Post-Training Quantization Methods for Language Tasks—0
Order of Compression: A Systematic and Optimal Sequence to Combinationally Compress CNN—0
An Ultra-Efficient Memristor-Based DNN Framework with Structured Weight Pruning and Quantization Using ADMM—0
Emotion Recognition Using Speaker Cues—0
Emergent Quantized Communication—0
Embedding Compression with Isotropic Iterative Quantization—0
Cell growth rate dictates the onset of glass to fluid-like transition and long time super-diffusion in an evolving cell colony—0
ANTLER: Bayesian Nonlinear Tensor Learning and Modeler for Unstructured, Varying-Size Point Cloud Data—0
Adaptive Periodic Averaging: A Practical Approach to Reducing Communication in Distributed Learning—0
Embedding Compression for Efficient Re-Identification—0
Embedded Phase Shifting: Robust Phase Shifting With Embedded Signals—0
CEGI: Measuring the trade-off between efficiency and carbon emissions for SLMs and VLMs—0
ELMGS: Enhancing memory and computation scaLability through coMpression for 3D Gaussian Splatting—0
CEG4N: Counter-Example Guided Neural Network Quantization Refinement—0
Elastic Significant Bit Quantization and Acceleration for Deep Neural Networks—0
CDQuant: Greedy Coordinate Descent for Accurate LLM Quantization—0
Ef-QuantFace: Streamlined Face Recognition with Small Data and Low-Bit Precision—0
EfQAT: An Efficient Framework for Quantization-Aware Training—0
CDC: Classification Driven Compression for Bandwidth Efficient Edge-Cloud Collaborative Deep Learning—0
An Overview on IEEE 802.11bf: WLAN Sensing—0
Efficient-VQGAN: Towards High-Resolution Image Generation with Efficient Vision Transformers—0
CBQ: Cross-Block Quantization for Large Language Models—0
Efficient Vision-based Vehicle Speed Estimation—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