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 2551–2600 of 4925 papers

TitleStatusHype
Line Spectrum Estimation and Detection with Few-bit ADCs: Theoretical Analysis and Generalized NOMP Algorithm—0
Analysis of the influence of final resolution on ADC accuracy—0
Q-YOLO: Efficient Inference for Real-time Object Detection—0
On a Relation Between the Rate-Distortion Function and Optimal Transport—0
Unlimited Sampling Radar: a Real-Time End-to-End Demonstrator—0
Designing strong baselines for ternary neural network quantization through support and mass equalization—0
ReLU Neural Networks, Polyhedral Decompositions, and Persistent Homolog—0
Analysis of Oversampling in Uplink Massive MIMO-OFDM with Low-Resolution ADCs—0
Rapid-INR: Storage Efficient CPU-free DNN Training Using Implicit Neural RepresentationCode0
A Structurally Regularized CNN Architecture via Adaptive Subband Decomposition—0
DNA-TEQ: An Adaptive Exponential Quantization of Tensors for DNN Inference—0
Efficient High-Resolution Template Matching with Vector Quantized Nearest Neighbour FieldsCode0
INR-MDSQC: Implicit Neural Representation Multiple Description Scalar Quantization for robust image Coding—0
Partitioning-Guided K-Means: Extreme Empty Cluster Resolution for Extreme Model Compression—0
QNNRepair: Quantized Neural Network Repair—0
Image storage on synthetic DNA using compressive autoencoders and DNA-adapted entropy coders—0
Subgraph Stationary Hardware-Software Inference Co-Design—0
An efficient and straightforward online quantization method for a data stream through remove-birth updatingCode0
DynaQuant: Compressing Deep Learning Training Checkpoints via Dynamic Quantization—0
Low-complexity Multidimensional DCT Approximations—0
Randomized Quantization is All You Need for Differential Privacy in Federated LearningCode0
Pushing the Limits of 3D Shape Generation at Scale—0
Dynamic Cell Modeling of Li-Ion Polymer Batteries for Precise SOC Estimation in Power-Needy Autonomous Electric Vehicles—0
Magnificent Minified Models—0
ZeRO++: Extremely Efficient Collective Communication for Giant Model Training—0
PUGAN: Physical Model-Guided Underwater Image Enhancement Using GAN with Dual-DiscriminatorsCode0
Neural Network Compression using Binarization and Few Full-Precision Weights—0
High-performance deep spiking neural networks with 0.3 spikes per neuron—0
GQFedWAvg: Optimization-Based Quantized Federated Learning in General Edge Computing SystemsCode0
Discrete Graph Auto-Encoder—0
MFSN: Multi-perspective Fusion Search Network For Pre-training Knowledge in Speech Emotion Recognition—0
Sparse-Inductive Generative Adversarial Hashing for Nearest Neighbor Search—0
Resource Efficient Neural Networks Using Hessian Based Pruning—0
End-to-End Neural Network Compression via _1_2 Regularized Latency Surrogates—0
Mixed-TD: Efficient Neural Network Accelerator with Layer-Specific Tensor DecompositionCode0
Precision-aware Latency and Energy Balancing on Multi-Accelerator Platforms for DNN Inference—0
Iterative Signal Processing for Integrated Sensing and Communication Systems—0
Augmenting Hessians with Inter-Layer Dependencies for Mixed-Precision Post-Training Quantization—0
Sensitivity-Aware Finetuning for Accuracy Recovery on Deep Learning Hardware—0
An Information-Theoretic Analysis of Self-supervised Discrete Representations of SpeechCode0
Temporal Dynamic Quantization for Diffusion Models—0
Modular Transformers: Compressing Transformers into Modularized Layers for Flexible Efficient Inference—0
Group channel pruning and spatial attention distilling for object detection—0
Dynamic quantized consensus under DoS attacks: Towards a tight zooming-out factor—0
On the Effectiveness of Hybrid Mutual Information Estimation—0
Quantization-Aware and Tensor-Compressed Training of Transformers for Natural Language Understanding—0
Towards Learning Discrete Representations via Self-Supervision for Wearables-Based Human Activity Recognition—0
FlexRound: Learnable Rounding based on Element-wise Division for Post-Training QuantizationCode0
Asymptotic Performance Analysis of Large-Scale Active IRS-Aided Wireless Network—0
PreQuant: A Task-agnostic Quantization Approach for Pre-trained Language Models—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