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 3401–3425 of 4925 papers

TitleStatusHype
Robustness Analysis of Deep Learning Frameworks on Mobile PlatformsCode0
Towards Energy-Efficient and Secure Edge AI: A Cross-Layer Framework—0
iRNN: Integer-only Recurrent Neural Network—0
QTTNet: Quantized Tensor Train Neural Networks for 3D Object and Video Recognition.Code0
Channel Estimation in MIMO Systems with One-bit Spatial Sigma-delta ADCs—0
Experimental implementation of a neural network optical channel equalizer in restricted hardware using pruning and quantization—0
Revisiting Quantization Error in Face Alignment—0
2-in-1 Accelerator: Enabling Random Precision Switch for Winning Both Adversarial Robustness and Efficiency—0
PHPQ: Pyramid Hybrid Pooling Quantization for Efficient Fine-Grained Image Retrieval—0
1st-Order Dynamics on Nonlinear Agents for Resource Allocation over Uniformly-Connected Networks—0
Bag of Tricks for Optimizing Transformer EfficiencyCode0
ECQ^x: Explainability-Driven Quantization for Low-Bit and Sparse DNNsCode0
Elastic Significant Bit Quantization and Acceleration for Deep Neural Networks—0
Self-supervised Product Quantization for Deep Unsupervised Image RetrievalCode0
Cluster-Promoting Quantization with Bit-Drop for Minimizing Network Quantization Loss—0
Risk Assessment for Connected Vehicles under Stealthy Attacks on Vehicle-to-Vehicle Networks—0
Characterization of the frequency response of channel-interleaved photonic ADCs based on the optical time-division demultiplexer—0
Quantized Convolutional Neural Networks Through the Lens of Partial Differential Equations—0
Quantization of Generative Adversarial Networks for Efficient Inference: a Methodological Study—0
4-bit Quantization of LSTM-based Speech Recognition Models—0
A Quantitative Approach To The Temporal Dependency in Video Coding—0
On Adaptive Transmission for Distributed Detection in Energy Harvesting Wireless Sensor Networks with Limited Fusion Center Feedback—0
Rate distortion comparison of a few gradient quantizers—0
On the Acceleration of Deep Neural Network Inference using Quantized Compressed Sensing—0
Reconfigurable co-processor architecture with limited numerical precision to accelerate deep convolutional 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