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 2651–2675 of 4925 papers

TitleStatusHype
Real-Time Distributed Model Predictive Control with Limited Communication Data Rates—0
Efficient Adaptive Activation Rounding for Post-Training Quantization—0
Optimal Brain Compression: A Framework for Accurate Post-Training Quantization and PruningCode1
Fast Nearest Convolution for Real-Time Efficient Image Super-ResolutionCode1
Joint Privacy Enhancement and Quantization in Federated LearningCode1
Adaptation of MobileNetV2 for Face Detection on Ultra-Low Power Platform—0
Are disentangled representations all you need to build speaker anonymization systems?—0
Combining Compressions for Multiplicative Size Scaling on Natural Language Tasks—0
DenseShift: Towards Accurate and Efficient Low-Bit Power-of-Two QuantizationCode1
What Does a One-Bit Quanta Image Sensor Offer?—0
G2P-DDM: Generating Sign Pose Sequence from Gloss Sequence with Discrete Diffusion Model—0
FP8 Quantization: The Power of the ExponentCode1
Towards Practical Single-shot Phase Retrieval with Physics-Driven Deep Neural Network—0
Outlier Detection using Self-Organizing Maps for Automated Blood Cell Analysis—0
Learning to Structure an Image with Few Colors and Beyond—0
AdaBin: Improving Binary Neural Networks with Adaptive Binary SetsCode0
Design and Analysis of Hardware-limited Non-uniform Task-based Quantizers—0
Adaptive Joint Optimization for 3D Reconstruction with Differentiable Rendering—0
LLM.int8(): 8-bit Matrix Multiplication for Transformers at ScaleCode5
PECAN: A Product-Quantized Content Addressable Memory Network—0
Sampled-data control design for systems with quantized actuators—0
Quantized Adaptive Subgradient Algorithms and Their Applications—0
Mixed-Precision Neural Networks: A Survey—0
Flexible Unsupervised Learning for Massive MIMO Subarray Hybrid Beamforming—0
Learning Quantization in LDPC Decoders—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