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 4501–4525 of 4925 papers

TitleStatusHype
FINN-L: Library Extensions and Design Trade-off Analysis for Variable Precision LSTM Networks on FPGAsCode0
Learning a Single Tucker Decomposition Network for Lossy Image Compression with Multiple Bits-Per-Pixel Rates—0
Two-stage iterative Procrustes match algorithm and its application for VQ-based speaker verification—0
Deep Saliency Hashing—0
OpenNMT System Description for WNMT 2018: 800 words/sec on a single-core CPU—0
SYQ: Learning Symmetric Quantization For Efficient Deep Neural NetworksCode0
An Exact Quantized Decentralized Gradient Descent Algorithm—0
Convolutional Neural Networks to Enhance Coded SpeechCode0
Segmentation of Overlapped Steatosis in Whole-Slide Liver Histopathology Microscopy Images—0
Distributed Average Consensus under Quantized Communication via Event-Triggered Mass Summation—0
Virtual Codec Supervised Re-Sampling Network for Image Compression—0
Quantizing deep convolutional networks for efficient inference: A whitepaperCode0
Error Compensated Quantized SGD and its Applications to Large-scale Distributed Optimization—0
Inference of Quantized Neural Networks on Heterogeneous All-Programmable Devices—0
Parcels of Universe or why Schr\"odinger and Fourier are so relatives?—0
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
RGCNN: Regularized Graph CNN for Point Cloud SegmentationCode0
Spreading vectors for similarity searchCode0
Deep Image Compression via End-to-End LearningCode0
Modeling Realistic Degradations in Non-blind Deconvolution—0
Playing Atari with Six NeuronsCode0
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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