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 2926–2950 of 4925 papers

TitleStatusHype
Boost CTR Prediction for New Advertisements via Modeling Visual Content—0
Performance Optimization for Variable Bitwidth Federated Learning in Wireless Networks—0
FoVolNet: Fast Volume Rendering using Foveated Deep Neural Networks—0
Flexible Neural Image Compression via Code Editing—0
SAMP: A Model Inference Toolkit of Post-Training Quantization for Text Processing via Self-Adaptive Mixed-Precision—0
PIM-QAT: Neural Network Quantization for Processing-In-Memory (PIM) Systems—0
Quantization for decentralized learning under subspace constraints—0
Compressed Particle-Based Federated Bayesian Learning and Unlearning—0
Analysis of Quantization on MLP-based Vision Models—0
SeRP: Self-Supervised Representation Learning Using Perturbed Point Clouds—0
In-situ animal behavior classification using knowledge distillation and fixed-point quantization—0
Compact and Robust Deep Learning Architecture for Fluorescence Lifetime Imaging and FPGA Implementation—0
A simple approach for quantizing neural networks—0
Towards Intelligent Millimeter and Terahertz Communication for 6G: Computer Vision-aided Beamforming—0
Optimized Precoding for MU-MIMO With Fronthaul Quantization—0
SaleNet: A low-power end-to-end CNN accelerator for sustained attention level evaluation using EEG—0
Low-Power Hardware-Based Deep-Learning Diagnostics Support Case Study—0
Augmented Deep Unfolding for Downlink Beamforming in Multi-cell Massive MIMO With Limited Feedback—0
PulseDL-II: A System-on-Chip Neural Network Accelerator for Timing and Energy Extraction of Nuclear Detector Signals—0
Human Activity Recognition on Microcontrollers with Quantized and Adaptive Deep Neural Networks—0
On Quantizing Implicit Neural Representations—0
XCAT -- Lightweight Quantized Single Image Super-Resolution using Heterogeneous Group Convolutions and Cross Concatenation—0
QuantNAS for super resolution: searching for efficient quantization-friendly architectures against quantization noiseCode0
Distributed CPU Scheduling Subject to Nonlinear Constraints—0
Distributed Constraint-Coupled Optimization over Lossy Networks—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