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 3126–3150 of 4925 papers

TitleStatusHype
Transformed Residual Quantization for Approximate Nearest Neighbor Search—0
Transformer-based Clipped Contrastive Quantization Learning for Unsupervised Image Retrieval—0
Transformer-based models and hardware acceleration analysis in autonomous driving: A survey—0
Transformer-Based Nonlinear Transform Coding for Multi-Rate CSI Compression in MIMO-OFDM Systems—0
Transformer-Lite: High-efficiency Deployment of Large Language Models on Mobile Phone GPUs—0
Transform Quantization for CNN (Convolutional Neural Network) Compression—0
Transition Rate Scheduling for Quantization-Aware Training—0
Transmitting Data Through Reconfigurable Intelligent Surface: A Spatial Sigma-Delta Modulation Approach—0
TR-DQ: Time-Rotation Diffusion Quantization—0
T-RECX: Tiny-Resource Efficient Convolutional neural networks with early-eXit—0
Tree Index: A New Cluster Evaluation Technique—0
Tree Quantization for Large-Scale Similarity Search and Classification—0
Triagem virtual de imagens de imuno-histoquímica usando redes neurais artificiais e espectro de padrões—0
TrimLLM: Progressive Layer Dropping for Domain-Specific LLMs—0
Trimming Down Large Spiking Vision Transformers via Heterogeneous Quantization Search—0
TriplePlay: Enhancing Federated Learning with CLIP for Non-IID Data and Resource Efficiency—0
TripleSpin - a generic compact paradigm for fast machine learning computations—0
TROJAN-GUARD: Hardware Trojans Detection Using GNN in RTL Designs—0
Truncated Non-Uniform Quantization for Distributed SGD—0
Semantic-Enabled 6G Communication: A Task-oriented and Privacy-preserving Perspective—0
TSPTQ-ViT: Two-scaled post-training quantization for vision transformer—0
TTAQ: Towards Stable Post-training Quantization in Continuous Domain Adaptation—0
TurboAttention: Efficient Attention Approximation For High Throughputs LLMs—0
Turbo-ICL: In-Context Learning-Based Turbo Equalization—0
TurboQuant: Online Vector Quantization with Near-optimal Distortion Rate—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