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

Showing 18011850 of 4925 papers

TitleStatusHype
Composite Code Sparse Autoencoders for first stage retrieval0
Are Words the Quanta of Human Language? Extending the Domain of Quantum Cognition0
Federated Learning With Quantized Global Model Updates0
Finetuning and Quantization of EEG-Based Foundational BioSignal Models on ECG and PPG Data for Blood Pressure Estimation0
Federated Learning with Lossy Distributed Source Coding: Analysis and Optimization0
FinGPT-HPC: Efficient Pretraining and Finetuning Large Language Models for Financial Applications with High-Performance Computing0
Component Training of Turbo Autoencoders0
RATQ: A Universal Fixed-Length Quantizer for Stochastic Optimization0
Federated Learning: Strategies for Improving Communication Efficiency0
CompMarkGS: Robust Watermarking for Compressed 3D Gaussian Splatting0
A Diffusion Model Based Quality Enhancement Method for HEVC Compressed Video0
Federated Learning in Adversarial Settings0
Completion Time Minimization of Fog-RAN-Assisted Federated Learning With Rate-Splitting Transmission0
FIXAR: A Fixed-Point Deep Reinforcement Learning Platform with Quantization-Aware Training and Adaptive Parallelism0
Federated Aggregation of Mallows Rankings: A Comparative Analysis of Borda and Lehmer Coding0
Fixed-point optimization of deep neural networks with adaptive step size retraining0
Compensate Quantization Errors+: Quantized Models Are Inquisitive Learners0
Fixed-point quantization aware training for on-device keyword-spotting0
A Review of Recent Advances of Binary Neural Networks for Edge Computing0
Fixed Point Quantization of Deep Convolutional Networks0
Fixflow: A Framework to Evaluate Fixed-point Arithmetic in Light-Weight CNN Inference0
FLARE: FP-Less PTQ and Low-ENOB ADC Based AMS-PiM for Error-Resilient, Fast, and Efficient Transformer Acceleration0
FedDQ: Communication-Efficient Federated Learning with Descending Quantization0
FlashAttention on a Napkin: A Diagrammatic Approach to Deep Learning IO-Awareness0
FedDM: Enhancing Communication Efficiency and Handling Data Heterogeneity in Federated Diffusion Models0
FlatENN: Train Flat for Enhanced Fault Tolerance of Quantized Deep Neural Networks0
FedDiSC: A Computation-efficient Federated Learning Framework for Power Systems Disturbance and Cyber Attack Discrimination0
Flattened one-bit stochastic gradient descent: compressed distributed optimization with controlled variance0
Fed-CVLC: Compressing Federated Learning Communications with Variable-Length Codes0
Compensate Quantization Errors: Make Weights Hierarchical to Compensate Each Other0
A review of learning vector quantization classifiers0
A Different View of Sigma-Delta Modulators Under the Lens of Pulse Frequency Modulation0
Flexible Unsupervised Learning for Massive MIMO Subarray Hybrid Beamforming0
FleXOR: Trainable Fractional Quantization0
3DQ: Compact Quantized Neural Networks for Volumetric Whole Brain Segmentation0
FedComLoc: Communication-Efficient Distributed Training of Sparse and Quantized Models0
FlightLLM: Efficient Large Language Model Inference with a Complete Mapping Flow on FPGAs0
FLightNNs: Lightweight Quantized Deep Neural Networks for Fast and Accurate Inference0
FedAQ: Communication-Efficient Federated Edge Learning via Joint Uplink and Downlink Adaptive Quantization0
Comparison of 14 different families of classification algorithms on 115 binary datasets0
Feature Quantization for Defending Against Distortion of Images0
FlowPrecision: Advancing FPGA-Based Real-Time Fluid Flow Estimation with Linear Quantization0
Comparing Iterative and Least-Squares Based Phase Noise Tracking in Receivers with 1-bit Quantization and Oversampling0
FlowVQTalker: High-Quality Emotional Talking Face Generation through Normalizing Flow and Quantization0
High-performance deep spiking neural networks with 0.3 spikes per neuron0
FoldToken2: Learning compact, invariant and generative protein structure language0
Comparing Fisher Information Regularization with Distillation for DNN Quantization0
Foothill: A Quasiconvex Regularization for Edge Computing of Deep Neural Networks0
Forearm Ultrasound based Gesture Recognition on Edge0
Feature Affinity Assisted Knowledge Distillation and Quantization of Deep Neural Networks on Label-Free Data0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1FQ-ViT (ViT-L)Top-1 Accuracy (%)85.03Unverified
2FQ-ViT (ViT-B)Top-1 Accuracy (%)83.31Unverified
3FQ-ViT (Swin-B)Top-1 Accuracy (%)82.97Unverified
4FQ-ViT (Swin-S)Top-1 Accuracy (%)82.71Unverified
5FQ-ViT (DeiT-B)Top-1 Accuracy (%)81.2Unverified
6FQ-ViT (Swin-T)Top-1 Accuracy (%)80.51Unverified
7FQ-ViT (DeiT-S)Top-1 Accuracy (%)79.17Unverified
8Xception W8A8Top-1 Accuracy (%)78.97Unverified
9ADLIK-MO-ResNet50-W4A4Top-1 Accuracy (%)77.88Unverified
10ADLIK-MO-ResNet50-W3A4Top-1 Accuracy (%)77.34Unverified
#ModelMetricClaimedVerifiedStatus
13DCNN_VIVA_3MAP160,327.04Unverified
2DTQMAP0.79Unverified
#ModelMetricClaimedVerifiedStatus
1OutEffHop-Bert_basePerplexity6.3Unverified
2OutEffHop-Bert_basePerplexity6.21Unverified
#ModelMetricClaimedVerifiedStatus
1Accuracy98.13Unverified
#ModelMetricClaimedVerifiedStatus
1Accuracy92.92Unverified
#ModelMetricClaimedVerifiedStatus
1SSD ResNet50 V1 FPN 640x640MAP34.3Unverified
#ModelMetricClaimedVerifiedStatus
1TAR @ FAR=1e-495.13Unverified
#ModelMetricClaimedVerifiedStatus
1TAR @ FAR=1e-496.38Unverified
#ModelMetricClaimedVerifiedStatus
13DCNN_VIVA_5All84,809,664Unverified
#ModelMetricClaimedVerifiedStatus
1Accuracy99.8Unverified