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 4526–4550 of 4925 papers

TitleStatusHype
GroupReduce: Block-Wise Low-Rank Approximation for Neural Language Model Shrinking—0
1-bit Localization Scheme for Radar using Dithered Quantized Compressed Sensing—0
Detecting Dead Weights and Units in Neural Networks—0
Scalable Neural Network Compression and Pruning Using Hard Clustering and L1 Regularization—0
Static Quantized Radix-2 FFT/IFFT Processor for Constraints Analysis—0
Spreading vectors for similarity searchCode0
RGCNN: Regularized Graph CNN for Point Cloud SegmentationCode0
Deep Image Compression via End-to-End LearningCode0
Playing Atari with Six NeuronsCode0
Modeling Realistic Degradations in Non-blind Deconvolution—0
Feature Quantization for Defending Against Distortion of Images—0
CLIP-Q: Deep Network Compression Learning by In-Parallel Pruning-Quantization—0
A Biresolution Spectral Framework for Product Quantization—0
Two-Step Quantization for Low-Bit Neural NetworksCode0
Explicit Loss-Error-Aware Quantization for Low-Bit Deep Neural Networks—0
Efficient Large-Scale Approximate Nearest Neighbor Search on OpenCL FPGA—0
MPDCompress - Matrix Permutation Decomposition Algorithm for Deep Neural Network Compression—0
Retraining-Based Iterative Weight Quantization for Deep Neural Networks—0
Convolutional neural network compression for natural language processing—0
Double Quantization for Communication-Efficient Distributed Optimization—0
Scalable Methods for 8-bit Training of Neural NetworksCode0
Deploy Large-Scale Deep Neural Networks in Resource Constrained IoT Devices with Local Quantization Region—0
Deep Learning Inference on Embedded Devices: Fixed-Point vs Posit—0
DEEPEYE: A Compact and Accurate Video Comprehension at Terminal Devices Compressed with Quantization and Tensorization—0
Compression of Deep Convolutional Neural Networks under Joint Sparsity Constraints—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