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 3526–3550 of 4925 papers

TitleStatusHype
Rescuing Deep Hashing from Dead Bits Problem—0
Understanding Cache Boundness of ML Operators on ARM ProcessorsCode0
CAMBI: Contrast-aware Multiscale Banding Index—0
Performance of Cell-Free MmWave Massive MIMO Systems with Fronthaul Compression and DAC Quantization—0
AdderNet and its Minimalist Hardware Design for Energy-Efficient Artificial Intelligence—0
Pruning and Quantization for Deep Neural Network Acceleration: A Survey—0
Error Diffusion Halftoning Against Adversarial ExamplesCode0
Continual Learning of Generative Models with Limited Data: From Wasserstein-1 Barycenter to Adaptive Coalescence—0
Generative Zero-shot Network Quantization—0
Overfitting for Fun and Profit: Instance-Adaptive Data Compression—0
Time-Correlated Sparsification for Communication-Efficient Federated Learning—0
SparseDNN: Fast Sparse Deep Learning Inference on CPUsCode1
ES-ENAS: Efficient Evolutionary Optimization for Large Hybrid Search SpacesCode0
Multi-Task Network Pruning and Embedded Optimization for Real-time Deployment in ADAS—0
Deep Compression of Neural Networks for Fault Detection on Tennessee Eastman Chemical Processes—0
KDLSQ-BERT: A Quantized Bert Combining Knowledge Distillation with Learned Step Size Quantization—0
On the quantization of recurrent neural networks—0
FBGEMM: Enabling High-Performance Low-Precision Deep Learning InferenceCode2
Energy-Efficient Distributed Learning Algorithms for Coarsely Quantized Signals—0
Fast convolutional neural networks on FPGAs with hls4mlCode2
Towards Energy Efficient Federated Learning over 5G+ Mobile Devices—0
Single-path Bit Sharing for Automatic Loss-aware Model Compression—0
Binary TTC: A Temporal Geofence for Autonomous NavigationCode1
Sound Event Detection with Binary Neural Networks on Tightly Power-Constrained IoT Devices—0
Activation Density based Mixed-Precision Quantization for Energy Efficient Neural 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