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 1476–1500 of 4925 papers

TitleStatusHype
Federated Split Learning with Model Pruning and Gradient Quantization in Wireless Networks—0
Fuzzy Norm-Explicit Product Quantization for Recommender Systems—0
Vision Transformer-based Semantic Communications With Importance-Aware Quantization—0
SizeGS: Size-aware Compression of 3D Gaussians with Hierarchical Mixed Precision Quantization—0
Taming Sensitive Weights : Noise Perturbation Fine-tuning for Robust LLM Quantization—0
Error Feedback Approach for Quantization Noise Reduction of Distributed Graph Filters—0
Sensor Selection and Distributed Quantization for Energy Efficiency in Massive MTC—0
GAQAT: gradient-adaptive quantization-aware training for domain generalization—0
Efficient Distributed Training through Gradient Compression with Sparsification and Quantization Techniques—0
Trimming Down Large Spiking Vision Transformers via Heterogeneous Quantization Search—0
ULMRec: User-centric Large Language Model for Sequential Recommendation—0
SKIM: Any-bit Quantization Pushing The Limits of Post-Training Quantization—0
Quantized and Interpretable Learning Scheme for Deep Neural Networks in Classification Task—0
Unifying KV Cache Compression for Large Language Models with LeanKV—0
FlashAttention on a Napkin: A Diagrammatic Approach to Deep Learning IO-Awareness—0
Prompting Large Language Models for Clinical Temporal Relation Extraction—0
Designing DNNs for a trade-off between robustness and processing performance in embedded devices—0
Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspectiveCode0
Mixed-Precision Quantization: Make the Best Use of Bits Where They Matter Most—0
CPTQuant -- A Novel Mixed Precision Post-Training Quantization Techniques for Large Language Models—0
3D representation in 512-Byte:Variational tokenizer is the key for autoregressive 3D generation—0
CEGI: Measuring the trade-off between efficiency and carbon emissions for SLMs and VLMs—0
Robust Precoding for Multi-User Visible Light Communications with Quantized Channel Information—0
Scaling Image Tokenizers with Grouped Spherical QuantizationCode0
Lean classical-quantum hybrid neural network model for image classification—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