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 4301–4325 of 4925 papers

TitleStatusHype
DTNN: Energy-efficient Inference with Dendrite Tree Inspired Neural Networks for Edge Vision Applications—0
Dual Codebook VQ: Enhanced Image Reconstruction with Reduced Codebook Size—0
Dual Grained Quantization: Efficient Fine-Grained Quantization for LLM—0
Dual Precision Quantization for Efficient and Accurate Deep Neural Networks Inference—0
DupNet: Towards Very Tiny Quantized CNN with Improved Accuracy for Face Detection—0
DV-Det: Efficient 3D Point Cloud Object Detection with Dynamic Voxelization—0
DyBit: Dynamic Bit-Precision Numbers for Efficient Quantized Neural Network Inference—0
Dynamic Cell Modeling of Li-Ion Polymer Batteries for Precise SOC Estimation in Power-Needy Autonomous Electric Vehicles—0
Adaptive Error-Bounded Hierarchical Matrices for Efficient Neural Network Compression—0
Dynamic Predictive Sampling Analog to Digital Converter for Sparse Signal Sensing—0
Dynamic Probabilistic Pruning: A general framework for hardware-constrained pruning at different granularities—0
Dynamic Q&A of Clinical Documents with Large Language Models—0
Dynamic Quantized Consensus of General Linear Multi-agent Systems under Denial-of-Service Attacks—0
Dynamic quantized consensus under DoS attacks: Towards a tight zooming-out factor—0
Dynamic Signal Measurements Based on Quantized Data—0
Dynamic Stashing Quantization for Efficient Transformer Training—0
DynaQuant: Compressing Deep Learning Training Checkpoints via Dynamic Quantization—0
DynScene: Scalable Generation of Dynamic Robotic Manipulation Scenes for Embodied AI—0
E^2BoWs: An End-to-End Bag-of-Words Model via Deep Convolutional Neural Network—0
EasyQuant: An Efficient Data-free Quantization Algorithm for LLMs—0
ECG Biometric Authentication Using Self-Supervised Learning for IoT Edge Sensors—0
EDAS: Efficient and Differentiable Architecture Search—0
Edge AI-Enabled Chicken Health Detection Based on Enhanced FCOS-Lite and Knowledge Distillation—0
Edge AI: Evaluation of Model Compression Techniques for Convolutional Neural Networks—0
EdgeBERT: Sentence-Level Energy Optimizations for Latency-Aware Multi-Task NLP Inference—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