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 1401–1425 of 4925 papers

TitleStatusHype
Abstractive summarization from Audio Transcription—0
Energy-efficient Knowledge Distillation for Spiking Neural Networks—0
Brain Inspired Cortical Coding Method for Fast Clustering and Codebook Generation—0
Effective Interplay between Sparsity and Quantization: From Theory to Practice—0
An Inter-Layer Weight Prediction and Quantization for Deep Neural Networks based on a Smoothly Varying Weight Hypothesis—0
Adaptive Compression for Communication-Efficient Distributed Training—0
Effective and Efficient Mixed Precision Quantization of Speech Foundation Models—0
HMDN: Hierarchical Multi-Distribution Network for Click-Through Rate Prediction—0
Energy Efficiency Optimization for Millimeter Wave System with Resolution-Adaptive ADCs—0
eDKM: An Efficient and Accurate Train-time Weight Clustering for Large Language Models—0
Edinburgh's Submissions to the 2020 Machine Translation Efficiency Task—0
Boosting Distributed Full-graph GNN Training with Asynchronous One-bit Communication—0
LCP: A Low-Communication Parallelization Method for Fast Neural Network Inference in Image Recognition—0
Non-linear Canonical Correlation Analysis: A Compressed Representation Approach—0
POLARON: Precision-aware On-device Learning and Adaptive Runtime-cONfigurable AI acceleration—0
Boosted Dense Retriever—0
Effective and Fast: A Novel Sequential Single Path Search for Mixed-Precision Quantization—0
Boost Vision Transformer with GPU-Friendly Sparsity and Quantization—0
Edge-MultiAI: Multi-Tenancy of Latency-Sensitive Deep Learning Applications on Edge—0
Boosted Dense Retriever—0
Effective Quantization Approaches for Recurrent Neural Networks—0
Effective Quantization for Diffusion Models on CPUs—0
EdgeMLOps: Operationalizing ML models with Cumulocity IoT and thin-edge.io for Visual quality Inspection—0
Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations—0
Energy awareness in low precision neural networks—0
Show:102550
← PrevPage 57 of 197Next →
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