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 1176–1200 of 4925 papers

TitleStatusHype
Domain Generalization on Efficient Acoustic Scene Classification using Residual Normalization—0
Don't Waste Your Bits! Squeeze Activations and Gradients for Deep Neural Networks via TinyScript—0
DoTA: Weight-Decomposed Tensor Adaptation for Large Language Models—0
Downlink Clustering-Based Scheduling of IRS-Assisted Communications With Reconfiguration Constraints—0
Communication-Efficient Decentralized Multi-Agent Reinforcement Learning for Cooperative Adaptive Cruise Control—0
A Quantization-based Technique for Privacy Preserving Distributed Learning—0
Communication Compression for Tensor Parallel LLM Inference—0
A Quantitative Approach To The Temporal Dependency in Video Coding—0
AdderNet and its Minimalist Hardware Design for Energy-Efficient Artificial Intelligence—0
3D Gaussian Splatting Data Compression with Mixture of Priors—0
Communication and Energy Efficient Federated Learning using Zero-Order Optimization Technique—0
COMET: Towards Partical W4A4KV4 LLMs Serving—0
A QP-adaptive Mechanism for CNN-based Filter in Video Coding—0
Post Training Quantization of Large Language Models with Microscaling Formats—0
Combining Compressions for Multiplicative Size Scaling on Natural Language Tasks—0
Accelerating Neural Network Inference by Overflow Aware Quantization—0
Collaborative Quantization for Cross-Modal Similarity Search—0
A Data and Compute Efficient Design for Limited-Resources Deep Learning—0
Collaborative Quantization Embeddings for Intra-Subject Prostate MR Image Registration—0
Collaborative Multi-Teacher Knowledge Distillation for Learning Low Bit-width Deep Neural Networks—0
APTQ: Attention-aware Post-Training Mixed-Precision Quantization for Large Language Models—0
Collaborative Filtering with Smooth Reconstruction of the Preference Function—0
Collaborative Edge AI Inference over Cloud-RAN—0
AdaQAT: Adaptive Bit-Width Quantization-Aware Training—0
Collaborative Automotive Radar Sensing via Mixed-Precision Distributed Array Completion—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