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 3776–3800 of 4925 papers

TitleStatusHype
Overcoming Forgetting Catastrophe in Quantization-Aware Training—0
Overcoming Representation Bias in Fairness-Aware data Repair using Optimal Transport—0
Overfitting for Fun and Profit: Instance-Adaptive Data Compression—0
Over-the-Air Computation via Cloud Radio Access Networks—0
OverQ: Opportunistic Outlier Quantization for Neural Network Accelerators—0
CAP: Correlation-Aware Pruning for Highly-Accurate Sparse Vision Models—0
P4Q: Learning to Prompt for Quantization in Visual-language Models—0
Packing and Padding: Coupled Multi-index for Accurate Image Retrieval—0
Pairwise Quantization—0
Pairwise Rotation Hashing for High-dimensional Features—0
PalmBench: A Comprehensive Benchmark of Compressed Large Language Models on Mobile Platforms—0
Panacea: Novel DNN Accelerator using Accuracy-Preserving Asymmetric Quantization and Energy-Saving Bit-Slice Sparsity—0
Paralinguistic Privacy Protection at the Edge—0
Parallelized Rate-Distortion Optimized Quantization Using Deep Learning—0
Parallel WiSARD object tracker: a ram-based tracking system—0
Parameterized Structured Pruning for Deep Neural Networks—0
Parametric context adaptive Laplace distribution for multimedia compression—0
Parcels of Universe or why Schr\"odinger and Fourier are so relatives?—0
Pareto Data Framework: Steps Towards Resource-Efficient Decision Making Using Minimum Viable Data (MVD)—0
PAROAttention: Pattern-Aware ReOrdering for Efficient Sparse and Quantized Attention in Visual Generation Models—0
PARQ: Piecewise-Affine Regularized Quantization—0
Parsimonious System Identification from Fragmented Quantized Measurements—0
Partitioning-Guided K-Means: Extreme Empty Cluster Resolution for Extreme Model Compression—0
PASCAL: Precise and Efficient ANN- SNN Conversion using Spike Accumulation and Adaptive Layerwise Activation—0
Passive Beamforming Design for Intelligent Reflecting Surface Assisted MIMO Systems—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