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 2801–2850 of 4925 papers

TitleStatusHype
FlatENN: Train Flat for Enhanced Fault Tolerance of Quantized Deep Neural Networks—0
Publishing Efficient On-device Models Increases Adversarial Vulnerability—0
Hyperspherical Loss-Aware Ternary Quantization—0
Development of a Thermodynamics of Human Cognition and Human Culture—0
Hyperspherical Quantization: Toward Smaller and More Accurate Models—0
EuclidNets: An Alternative Operation for Efficient Inference of Deep Learning Models—0
Cross-Dataset Propensity Estimation for Debiasing Recommender Systems—0
Training Integer-Only Deep Recurrent Neural Networks—0
Automatic Network Adaptation for Ultra-Low Uniform-Precision Quantization—0
Towards Neural Variational Monte Carlo That Scales Linearly with System Size—0
CSMPQ:Class Separability Based Mixed-Precision Quantization—0
Redistribution of Weights and Activations for AdderNet Quantization—0
Model Predictive Control for Neuromimetic Quantized Systems—0
Atrous Space Bender U-Net (ASBU-Net/LogiNet)—0
Joint SPX-VIX calibration with Gaussian polynomial volatility models: deep pricing with quantization hints—0
NAWQ-SR: A Hybrid-Precision NPU Engine for Efficient On-Device Super-Resolution—0
Huber-energy measure quantization—0
Towards Hardware-Specific Automatic Compression of Neural Networks—0
Image Compression with Product Quantized Masked Image Modeling—0
Efficient Speech Representation Learning with Low-Bit Quantization—0
Adaptive Low-Precision Training for Embeddings in Click-Through Rate Prediction—0
ResFed: Communication Efficient Federated Learning by Transmitting Deep Compressed Residuals—0
Error-aware Quantization through Noise Tempering—0
QVIP: An ILP-based Formal Verification Approach for Quantized Neural NetworksCode0
Vertical Layering of Quantized Neural Networks for Heterogeneous Inference—0
Genie: Show Me the Data for QuantizationCode0
TinyKG: Memory-Efficient Training Framework for Knowledge Graph Neural Recommender Systems—0
QEBVerif: Quantization Error Bound Verification of Neural NetworksCode0
CSQ: Growing Mixed-Precision Quantization Scheme with Bi-level Continuous Sparsification—0
Quantized Wasserstein Procrustes Alignment of Word Embedding Spaces—0
QFT: Post-training quantization via fast joint finetuning of all degrees of freedom—0
Make RepVGG Greater Again: A Quantization-aware ApproachCode0
Device Interoperability for Learned Image Compression with Weights and Activations Quantization—0
Shining light on data: Geometric data analysis through quantum dynamics—0
Robust Task-Specific Beamforming with Low-Resolution ADCs for Power-Efficient Hybrid MIMO Receivers—0
Quadapter: Adapter for GPT-2 Quantization—0
Quantization-aware Interval Bound Propagation for Training Certifiably Robust Quantized Neural NetworksCode0
Distributed Computation of Exact Average Degree and Network Size in Finite Number of Steps under Quantized Communication—0
Quantized control of non-Lipschitz nonlinear systems: a novel control framework with prescribed transient performance and lower design complexity—0
Entropy-Driven Mixed-Precision Quantization for Deep Network Design—0
Class-based Quantization for Neural Networks—0
Homology-constrained vector quantization entropy regularizerCode0
Vector Quantization Methods for Access Point Placement in Cell-Free Massive MIMO Systems—0
A 65nm 8b-Activation 8b-Weight SRAM-Based Charge-Domain Computing-in-Memory Macro Using A Fully-Parallel Analog Adder Network and A Single-ADC Interface—0
RIS-Assisted Self-Interference Mitigation for In-Band Full-Duplex Transceivers—0
High-Perceptual Quality JPEG Decoding via Posterior Sampling—0
Learning Low-Rank Representations for Model Compression—0
Understanding and Improving Knowledge Distillation for Quantization-Aware Training of Large Transformer EncodersCode0
A Closed-loop Sleep Modulation System with FPGA-Accelerated Deep Learning—0
Deep Learning-Aided Perturbation Model-Based Fiber Nonlinearity Compensation—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