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 2476–2500 of 4925 papers

TitleStatusHype
Quantized Decoder in Learned Image Compression for Deterministic Reconstruction—0
Quantized Deep Path-following Control on a Microcontroller—0
Quantized Delta Weight Is Safety Keeper—0
Quantized Dissipative Uncertain Model for Fractional T_S Fuzzy systems with Time_Varying Delays Under Networked Control System—0
Quantized distributed Nash equilibrium seeking under DoS attacks—0
Quantized Distributed Training of Large Models with Convergence Guarantees—0
Quantized Embedding Vectors for Controllable Diffusion Language Models—0
Quantized Epoch-SGD for Communication-Efficient Distributed Learning—0
Quantized Feature Distillation for Network Quantization—0
Quantized Federated Learning under Transmission Delay and Outage Constraints—0
Quantized Frank-Wolfe: Faster Optimization, Lower Communication, and Projection Free—0
Quantized Guided Pruning for Efficient Hardware Implementations of Convolutional Neural Networks—0
A Hierarchical Federated Learning Approach for the Internet of Things—0
Quantized Kernel Learning for Feature Matching—0
Quantized Low-Rank Multivariate Regression with Random Dithering—0
Quantized Memory-Augmented Neural Networks—0
Quantized Minimum Error Entropy Criterion—0
Quantized neural network design under weight capacity constraint—0
Quantized neural network for complex hologram generation—0
Quantized Neural Network Inference with Precision Batching—0
Quantized Neural Networks: Characterization and Holistic Optimization—0
Quantized Neural Networks for Low-Precision Accumulation with Guaranteed Overflow Avoidance—0
Quantized Neural Networks for Radar Interference Mitigation—0
Quantized Nonparametric Estimation over Sobolev Ellipsoids—0
Quantized Precoding and RIS-Assisted Modulation for Integrated Sensing and Communications Systems—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