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 11261150 of 4925 papers

TitleStatusHype
Reputation-Driven Asynchronous Federated Learning for Enhanced Trajectory Prediction with Blockchain0
The Interpretability of Codebooks in Model-Based Reinforcement Learning is Limited0
Mixed Non-linear Quantization for Vision TransformersCode0
Quasar-ViT: Hardware-Oriented Quantization-Aware Architecture Search for Vision Transformers0
Unlocking Tokens as Data Points for Generalization Bounds on Larger Language Models0
Accurate and Efficient Fine-Tuning of Quantized Large Language Models Through Optimal BalanceCode0
Low dimensional representation of multi-patient flow cytometry datasets using optimal transport for minimal residual disease detection in leukemiaCode0
Pixel Embedding: Fully Quantized Convolutional Neural Network with Differentiable Lookup Table0
Comprehensive Study on Performance Evaluation and Optimization of Model Compression: Bridging Traditional Deep Learning and Large Language Models0
Compensate Quantization Errors+: Quantized Models Are Inquisitive Learners0
Differentiable Product Quantization for Memory Efficient Camera RelocalizationCode0
Uplink Transmit Power Optimization for Distributed Massive MIMO Systems with 1-Bit ADCs0
Power Measurement Enabled Channel Autocorrelation Matrix Estimation for IRS-Assisted Wireless Communication0
MetaAug: Meta-Data Augmentation for Post-Training QuantizationCode0
FedDM: Enhancing Communication Efficiency and Handling Data Heterogeneity in Federated Diffusion Models0
A Benchmark for Gaussian Splatting Compression and Quality Assessment StudyCode1
Mixed-precision Neural Networks on RISC-V Cores: ISA extensions for Multi-Pumped Soft SIMD OperationsCode1
Mixture of Experts with Mixture of Precisions for Tuning Quality of Service0
Asymptotically Optimal Closed-Form Phase Configuration of 1-bit RISs via Sign Alignment0
LiNR: Model Based Neural Retrieval on GPUs at LinkedIn0
MCU-MixQ: A HW/SW Co-optimized Mixed-precision Neural Network Design Framework for MCUs0
SmartQuant: CXL-based AI Model Store in Support of Runtime Configurable Weight Quantization0
AdaLog: Post-Training Quantization for Vision Transformers with Adaptive Logarithm QuantizerCode1
Spectra: Surprising Effectiveness of Pretraining Ternary Language Models at ScaleCode2
Toward INT4 Fixed-Point Training via Exploring Quantization Error for Gradients0
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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