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 3626–3650 of 4925 papers

TitleStatusHype
An Ultra-Efficient Memristor-Based DNN Framework with Structured Weight Pruning and Quantization Using ADMM—0
APack: Off-Chip, Lossless Data Compression for Efficient Deep Learning Inference—0
APCodec+: A Spectrum-Coding-Based High-Fidelity and High-Compression-Rate Neural Audio Codec with Staged Training Paradigm—0
A Performance Analysis of You Only Look Once Models for Deployment on Constrained Computational Edge Devices in Drone Applications—0
APG-MOS: Auditory Perception Guided-MOS Predictor for Synthetic Speech—0
A Picture is Worth a Billion Bits: Real-Time Image Reconstruction from Dense Binary Pixels—0
A Planck Radiation and Quantization Scheme for Human Cognition and Language—0
Apollo-Forecast: Overcoming Aliasing and Inference Speed Challenges in Language Models for Time Series Forecasting—0
A Post-coder Feedback Approach to Overcome Training Asymmetry in MIMO-TDD—0
Approaching Rate-Distortion Limits in Neural Compression with Lattice Transform Coding—0
Approximate DCT and Quantization Techniques for Energy-Constrained Image Sensors—0
Approximately Invertible Neural Network for Learned Image Compression—0
Approximate Probabilistic Neural Networks with Gated Threshold Logic—0
Approximate search with quantized sparse representations—0
Approximation of functions with one-bit neural networks—0
Approximation speed of quantized vs. unquantized ReLU neural networks and beyond—0
A Practical Mixed Precision Algorithm for Post-Training Quantization—0
A Precision-Scalable RISC-V DNN Processor with On-Device Learning Capability at the Extreme Edge—0
A Principled Hierarchical Deep Learning Approach to Joint Image Compression and Classification—0
A Privacy Preserving System for Movie Recommendations Using Federated Learning—0
A Probabilistic Reformulation Technique for Discrete RIS Optimization in Wireless Systems—0
APTQ: Attention-aware Post-Training Mixed-Precision Quantization for Large Language Models—0
A QP-adaptive Mechanism for CNN-based Filter in Video Coding—0
A Quantitative Approach To The Temporal Dependency in Video Coding—0
A Quantization-based Technique for Privacy Preserving Distributed Learning—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