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 1976–2000 of 4925 papers

TitleStatusHype
Global synchronization of multi-agent systems with nonlinear interactions—0
Goal-oriented compression for L_p-norm-type goal functions: Application to power consumption scheduling—0
Goal-Oriented Quantization: Analysis, Design, and Application to Resource Allocation—0
Fast Low-rank Representation based Spatial Pyramid Matching for Image Classification—0
GOBO: Quantizing Attention-Based NLP Models for Low Latency and Energy Efficient Inference—0
Communication-Efficient Split Learning via Adaptive Feature-Wise Compression—0
Going Further With Winograd Convolutions: Tap-Wise Quantization for Efficient Inference on 4x4 Tile—0
Google USM: Scaling Automatic Speech Recognition Beyond 100 Languages—0
Fast learning rates with heavy-tailed losses—0
gpcgc: a green point cloud geometry coding method—0
GPLQ: A General, Practical, and Lightning QAT Method for Vision Transformers—0
Fast Large-Scale Discrete Optimization Based on Principal Coordinate Descent—0
GPTQT: Quantize Large Language Models Twice to Push the Efficiency—0
Fast Jet Tagging with MLP-Mixers on FPGAs—0
Fast Inference of Tree Ensembles on ARM Devices—0
Communication Efficient SGD via Gradient Sampling With Bayes Prior—0
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
AddNet: Deep Neural Networks Using FPGA-Optimized Multipliers—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
Fast Implementation of 4-bit Convolutional Neural Networks for Mobile Devices—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