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 4151–4175 of 4925 papers

TitleStatusHype
QuaRL: Quantization for Fast and Environmentally Sustainable Reinforcement LearningCode0
Hierarchical Encoding of Sequential Data With Compact and Sub-Linear Storage CostCode0
NGEMM: Optimizing GEMM for Deep Learning via Compiler-based Techniques—0
DSConv: Efficient Convolution Operator—0
Automated design of error-resilient and hardware-efficient deep neural networks—0
XNOR-Net++: Improved Binary Neural Networks—0
Optimal Controller and Quantizer Selection for Partially Observable Linear-Quadratic-Gaussian Systems—0
AdaptivFloat: A Floating-point based Data Type for Resilient Deep Learning Inference—0
REQ-YOLO: A Resource-Aware, Efficient Quantization Framework for Object Detection on FPGAs—0
Additive Powers-of-Two Quantization: An Efficient Non-uniform Discretization for Neural NetworksCode0
FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization—0
Optimized Quantization in Distributed Graph Signal Filtering—0
Impact of Low-bitwidth Quantization on the Adversarial Robustness for Embedded Neural Networks—0
GA-GAN: CT reconstruction from Biplanar DRRs using GAN with Guided Attention—0
Optimizing Speech Recognition For The Edge—0
Adaptive Binary-Ternary Quantization—0
Goten: GPU-Outsourcing Trusted Execution of Neural Network Training and PredictionCode0
Hybrid Weight Representation: A Quantization Method Represented with Ternary and Sparse-Large Weights—0
QGAN: Quantize Generative Adversarial Networks to Extreme low-bits—0
Rethinking Neural Network Quantization—0
Prune or quantize? Strategy for Pareto-optimally low-cost and accurate CNN—0
Provably Communication-efficient Data-parallel SGD via Nonuniform Quantization—0
Smart Ternary Quantization—0
Lattice Representation Learning—0
CURSOR-BASED ADAPTIVE QUANTIZATION FOR DEEP NEURAL NETWORK—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