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 1251–1275 of 4925 papers

TitleStatusHype
Approximate Probabilistic Neural Networks with Gated Threshold Logic—0
Adaptive Sample-space & Adaptive Probability coding: a neural-network based approach for compression—0
Towards Feature Distribution Alignment and Diversity Enhancement for Data-Free Quantization—0
Cluster Pruning: An Efficient Filter Pruning Method for Edge AI Vision Applications—0
Adaptive Resource Allocation for Semantic Communication Networks—0
Cluster-Promoting Quantization with Bit-Drop for Minimizing Network Quantization Loss—0
Clustering with Bregman Divergences: an Asymptotic Analysis—0
Approximately Invertible Neural Network for Learned Image Compression—0
Adaptive Resolution Inference (ARI): Energy-Efficient Machine Learning for Internet of Things—0
Clustering-Based Evolutionary Federated Multiobjective Optimization and Learning—0
Approximate DCT and Quantization Techniques for Energy-Constrained Image Sensors—0
Cluster-Based Cooperative Digital Over-the-Air Aggregation for Wireless Federated Edge Learning—0
ClusComp: A Simple Paradigm for Model Compression and Efficient Finetuning—0
Approaching Rate-Distortion Limits in Neural Compression with Lattice Transform Coding—0
1-Bit Compressive Sensing for Efficient Federated Learning Over the Air—0
Effective and Efficient Mixed Precision Quantization of Speech Foundation Models—0
Effective Interplay between Sparsity and Quantization: From Theory to Practice—0
Accelerating Deep Learning with Dynamic Data Pruning—0
CLIP-Q: Deep Network Compression Learning by In-Parallel Pruning-Quantization—0
Edge-MultiAI: Multi-Tenancy of Latency-Sensitive Deep Learning Applications on Edge—0
Adaptive quantization with mixed-precision based on low-cost proxy—0
2-Bit Random Projections, NonLinear Estimators, and Approximate Near Neighbor Search—0
A Post-coder Feedback Approach to Overcome Training Asymmetry in MIMO-TDD—0
Click-through Rate Prediction with Auto-Quantized Contrastive Learning—0
Adaptive Quantization Resolution and Power Control for Federated Learning over Cell-free Networks—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