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 3251–3275 of 4925 papers

TitleStatusHype
On the Acceleration of Deep Neural Network Inference using Quantized Compressed Sensing—0
Rate distortion comparison of a few gradient quantizers—0
Reconfigurable co-processor architecture with limited numerical precision to accelerate deep convolutional neural networks—0
Integer-arithmetic-only Certified Robustness for Quantized Neural Networks—0
Quantization Backdoors to Deep Learning Commercial Frameworks—0
Verifying Low-dimensional Input Neural Networks via Input Quantization—0
Deployment of Deep Neural Networks for Object Detection on Edge AI Devices with Runtime Optimization—0
Distance-aware Quantization—0
FOX-NAS: Fast, On-device and Explainable Neural Architecture SearchCode1
Pruning vs XNOR-Net: A Comprehensive Study of Deep Learning for Audio Classification on Edge-devicesCode0
Audio Spectral Enhancement: Leveraging Autoencoders for Low Latency Reconstruction of Long, Lossy Audio SequencesCode0
Energy Efficiency Maximization Precoding for Quantized Massive MIMO Systems—0
Generalizable Mixed-Precision Quantization via Attribution Rank PreservationCode1
Bifocal Neural ASR: Exploiting Keyword Spotting for Inference Optimization—0
MOHAQ: Multi-Objective Hardware-Aware Quantization of Recurrent Neural Networks—0
All-Digital LoS MIMO with Low-Precision Analog-to-Digital Conversion—0
Communication-Efficient Federated Learning via Predictive CodingCode0
Jointly Optimizing Query Encoder and Product Quantization to Improve Retrieval PerformanceCode1
DQ-SGD: Dynamic Quantization in SGD for Communication-Efficient Distributed Learning—0
Connecting Compression Spaces with Transformer for Approximate Nearest Neighbor Search—0
Local Morphometry of Closed, Implicit Surfaces—0
Sparse Joint Transmission for Cloud Radio Access Networks with Limited Fronthaul Capacity—0
QuPeD: Quantized Personalization via Distillation with Applications to Federated Learning—0
Adaptive Precision Training (AdaPT): A dynamic fixed point quantized training approach for DNNs—0
Uniformity in Heterogeneity:Diving Deep into Count Interval Partition for Crowd CountingCode1
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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