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 2726–2750 of 4925 papers

TitleStatusHype
CA-SpaceNet: Counterfactual Analysis for 6D Pose Estimation in SpaceCode1
S4: a High-sparsity, High-performance AI Accelerator—0
Low-bit Shift Network for End-to-End Spoken Language Understanding—0
Semi-supervised Vector-Quantization in Visual SLAM using HGCN—0
T-RECX: Tiny-Resource Efficient Convolutional neural networks with early-eXit—0
Lipschitz Continuity Retained Binary Neural NetworkCode0
Learning Representations for CSI Adaptive Quantization and Feedback—0
Sub 8-Bit Quantization of Streaming Keyword Spotting Models for Embedded Chipsets—0
Collaborative Quantization Embeddings for Intra-Subject Prostate MR Image Registration—0
Hybrid Spatial-Temporal Entropy Modelling for Neural Video Compression—0
DiverGet: A Search-Based Software Testing Approach for Deep Neural Network Quantization Assessment—0
Synergistic Self-supervised and Quantization LearningCode1
Sparsifying Binary Networks—0
CEG4N: Counter-Example Guided Neural Network Quantization Refinement—0
Attention Round for Post-Training Quantization—0
Cross-Scale Vector Quantization for Scalable Neural Speech Coding—0
Network Binarization via Contrastive LearningCode1
BiTAT: Neural Network Binarization with Task-dependent Aggregated Transformation—0
I-ViT: Integer-only Quantization for Efficient Vision Transformer InferenceCode2
Quantum Neural Network Compression—0
Task-Oriented Sensing, Computation, and Communication Integration for Multi-Device Edge AI—0
QUIDAM: A Framework for Quantization-Aware DNN Accelerator and Model Co-Exploration—0
On-Device Training Under 256KB MemoryCode2
Sub-8-Bit Quantization Aware Training for 8-Bit Neural Network Accelerator with On-Device Speech Recognition—0
Compressing Pre-trained Transformers via Low-Bit NxM Sparsity for Natural Language Understanding—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