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 1401–1425 of 4925 papers

TitleStatusHype
FTT-NAS: Discovering Fault-Tolerant Convolutional Neural ArchitectureCode0
NITRO-D: Native Integer-only Training of Deep Convolutional Neural NetworksCode0
GANQ: GPU-Adaptive Non-Uniform Quantization for Large Language ModelsCode0
Deep Learning Models in Speech Recognition: Measuring GPU Energy Consumption, Impact of Noise and Model Quantization for Edge DeploymentCode0
A Comprehensive Evaluation of Quantization Strategies for Large Language ModelsCode0
FPQVAR: Floating Point Quantization for Visual Autoregressive Model with FPGA Hardware Co-designCode0
Deep Learning-Based Quantization of L-Values for Gray-Coded ModulationCode0
FP4DiT: Towards Effective Floating Point Quantization for Diffusion TransformersCode0
FLoCoRA: Federated learning compression with low-rank adaptationCode0
Autoregressive Co-Training for Learning Discrete Speech RepresentationsCode0
FlexRound: Learnable Rounding based on Element-wise Division for Post-Training QuantizationCode0
Floating-Point Quantization Analysis of Multi-Layer Perceptron Artificial Neural NetworksCode0
Deep Learning as a Mixed Convex-Combinatorial Optimization ProblemCode0
On-Device LLM for Context-Aware Wi-Fi RoamingCode0
Flexible framework for audio reconstructionCode0
FlashEval: Towards Fast and Accurate Evaluation of Text-to-image Diffusion Generative ModelsCode0
An Integrated Approach to Produce Robust Models with High EfficiencyCode0
Effective Communication with Dynamic Feature CompressionCode0
Flexible Mixed Precision Quantization for Learned Image CompressionCode0
Foundations of Large Language Model Compression -- Part 1: Weight QuantizationCode0
Adaptive Computation Modules: Granular Conditional Computation For Efficient InferenceCode0
Deep Image Compression via End-to-End LearningCode0
Find the Lady: Permutation and Re-Synchronization of Deep Neural NetworksCode0
Effective Quantization Methods for Recurrent Neural NetworksCode0
Filtering Empty Camera Trap Images in Embedded SystemsCode0
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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