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 3326–3350 of 4925 papers

TitleStatusHype
WRPN: Training and Inference using Wide Reduced-Precision Networks—0
WSMN: An optimized multipurpose blind watermarking in Shearlet domain using MLP and NSGA-II—0
WSNet: Compact and Efficient Networks Through Weight Sampling—0
WSNet: Learning Compact and Efficient Networks with Weight Sampling—0
Wyner-Ziv Gradient Compression for Federated Learning—0
XCAT -- Lightweight Quantized Single Image Super-Resolution using Heterogeneous Group Convolutions and Cross Concatenation—0
XNORBIN: A 95 TOp/s/W Hardware Accelerator for Binary Convolutional Neural Networks—0
XNOR-Net++: Improved Binary Neural Networks—0
YONO: Modeling Multiple Heterogeneous Neural Networks on Microcontrollers—0
You Never Know: Quantization Induces Inconsistent Biases in Vision-Language Foundation Models—0
YUVMultiNet: Real-time YUV multi-task CNN for autonomous driving—0
Consistent Signal Reconstruction from Streaming Multivariate Time Series—0
Zero-Delay Gaussian Joint Source-Channel Coding for the Interference Channel—0
FDC: Fast KV Dimensionality Compression for Efficient LLM Inference—0
ZeRO++: Extremely Efficient Collective Communication for Giant Model Training—0
ZeroQuant-FP: A Leap Forward in LLMs Post-Training W4A8 Quantization Using Floating-Point Formats—0
ZeroQuant-HERO: Hardware-Enhanced Robust Optimized Post-Training Quantization Framework for W8A8 Transformers—0
Zero-shot Adversarial Quantization—0
Zero-Shot Learning of a Conditional Generative Adversarial Network for Data-Free Network Quantization—0
Zero-shot Quantization: A Comprehensive Survey—0
Zero-Shot Sharpness-Aware Quantization for Pre-trained Language Models—0
Zeroth-Order Fine-Tuning of LLMs with Extreme Sparsity—0
ZipML: Training Linear Models with End-to-End Low Precision, and a Little Bit of Deep Learning—0
ZipVL: Efficient Large Vision-Language Models with Dynamic Token Sparsification—0
ZOBNN: Zero-Overhead Dependable Design of Binary Neural Networks with Deliberately Quantized Parameters—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