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 2201–2225 of 4925 papers

TitleStatusHype
A Counterexample in Cross-Correlation Template Matching—0
Inverted Semantic-Index for Image Retrieval—0
Investigating Automatic Scoring and Feedback using Large Language Models—0
Development of Quantized DNN Library for Exact Hardware Emulation—0
Development of a Thermodynamics of Human Cognition and Human Culture—0
BEAST: Efficient Tokenization of B-Splines Encoded Action Sequences for Imitation Learning—0
AMXFP4: Taming Activation Outliers with Asymmetric Microscaling Floating-Point for 4-bit LLM Inference—0
Intuitive Analysis of the Quantization-based Optimization: From Stochastic and Quantum Mechanical Perspective—0
Investigating Disentanglement in a Phoneme-level Speech Codec for Prosody Modeling—0
Detection of small changes in medical and random-dot images comparing self-organizing map performance to human detection—0
BeamVQ: Aligning Space-Time Forecasting Model via Self-training on Physics-aware Metrics—0
A 71.2-μW Speech Recognition Accelerator with Recurrent Spiking Neural Network—0
DHNet: Double MPEG-4 Compression Detection via Multiple DCT Histograms—0
Detecting Face Synthesis Using a Concealed Fusion Model—0
A Cost-Efficient FPGA Implementation of Tiny Transformer Model using Neural ODE—0
InTreeger: An End-to-End Framework for Integer-Only Decision Tree Inference—0
Detecting Dead Weights and Units in Neural Networks—0
BdSLW401: Transformer-Based Word-Level Bangla Sign Language Recognition Using Relative Quantization Encoding (RQE)—0
A multi-layer image representation using Regularized Residual Quantization: application to compression and denoising—0
Design Space Exploration of Low-Bit Quantized Neural Networks for Visual Place Recognition—0
Design Space Exploration of Dense and Sparse Mapping Schemes for RRAM Architectures—0
BDD4BNN: A BDD-based Quantitative Analysis Framework for Binarized Neural Networks—0
Intriguing Properties of Quantization at Scale—0
Investigating the Impact of Quantization on Adversarial Robustness—0
Design of Stochastic Quantizers for Privacy Preservation—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