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 4726–4750 of 4925 papers

TitleStatusHype
GQ-Net: Training Quantization-Friendly Deep Networks—0
GQSA: Group Quantization and Sparsity for Accelerating Large Language Model Inference—0
GradFreeBits: Gradient Free Bit Allocation for Dynamic Low Precision Neural Networks—0
WaveQ: Gradient-Based Deep Quantization of Neural Networks through Sinusoidal Adaptive Regularization—0
Gradient Based Method for the Fusion of Lattice Quantizers—0
Gradient-Based Post-Training Quantization: Challenging the Status Quo—0
Gradient Descent Quantizes ReLU Network Features—0
Gradient Diversity: a Key Ingredient for Scalable Distributed Learning—0
Gradient _1 Regularization for Quantization Robustness—0
Gradient-Free Neural Network Training on the Edge—0
Gradient Obfuscation Gives a False Sense of Security in Federated Learning—0
GradiVeQ: Vector Quantization for Bandwidth-Efficient Gradient Aggregation in Distributed CNN Training—0
Gradual Binary Search and Dimension Expansion : A general method for activation quantization in LLMs—0
Granger Causality from Quantized Measurements—0
GranQ: Granular Zero-Shot Quantization with Channel-Wise Activation Scaling in QAT—0
Graph-Based Depth Denoising & Dequantization for Point Cloud Enhancement—0
Graph-Collaborated Auto-Encoder Hashing for Multi-view Binary Clustering—0
Greedy Selection for Heterogeneous Sensors—0
Greener yet Powerful: Taming Large Code Generation Models with Quantization—0
Green My LLM: Studying the key factors affecting the energy consumption of code assistants—0
Green, Quantized Federated Learning over Wireless Networks: An Energy-Efficient Design—0
Grid Based Nonlinear Filtering Revisited: Recursive Estimation & Asymptotic Optimality—0
Gridless Angular Domain Channel Estimation for mmWave Massive MIMO System With One-Bit Quantization Via Approximate Message Passing—0
Gridless Multisnapshot Variational Line Spectral Estimation from Coarsely Quantized Samples—0
Group channel pruning and spatial attention distilling for object detection—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