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 2626–2650 of 4925 papers

TitleStatusHype
FP8 Formats for Deep LearningCode4
In-situ animal behavior classification using knowledge distillation and fixed-point quantization—0
A simple approach for quantizing neural networks—0
Compact and Robust Deep Learning Architecture for Fluorescence Lifetime Imaging and FPGA Implementation—0
Generative Adversarial Super-Resolution at the Edge with Knowledge DistillationCode1
YOLOv6: A Single-Stage Object Detection Framework for Industrial ApplicationsCode5
Towards Intelligent Millimeter and Terahertz Communication for 6G: Computer Vision-aided Beamforming—0
Optimized Precoding for MU-MIMO With Fronthaul Quantization—0
Low-Power Hardware-Based Deep-Learning Diagnostics Support Case Study—0
SaleNet: A low-power end-to-end CNN accelerator for sustained attention level evaluation using EEG—0
Augmented Deep Unfolding for Downlink Beamforming in Multi-cell Massive MIMO With Limited Feedback—0
Human Activity Recognition on Microcontrollers with Quantized and Adaptive Deep Neural Networks—0
PulseDL-II: A System-on-Chip Neural Network Accelerator for Timing and Energy Extraction of Nuclear Detector Signals—0
On Quantizing Implicit Neural Representations—0
QuantNAS for super resolution: searching for efficient quantization-friendly architectures against quantization noiseCode0
XCAT -- Lightweight Quantized Single Image Super-Resolution using Heterogeneous Group Convolutions and Cross Concatenation—0
Distributed CPU Scheduling Subject to Nonlinear Constraints—0
ANT: Exploiting Adaptive Numerical Data Type for Low-bit Deep Neural Network QuantizationCode1
Convergence Rates for Regularized Optimal Transport via Quantization—0
Distributed Constraint-Coupled Optimization over Lossy Networks—0
Computing with Hypervectors for Efficient Speaker Identification—0
Lossy Image Compression with Quantized Hierarchical VAEsCode1
Reducing Computational Complexity of Neural Networks in Optical Channel Equalization: From Concepts to Implementation—0
Ab-initio quantum chemistry with neural-network wavefunctions—0
GHN-Q: Parameter Prediction for Unseen Quantized Convolutional Architectures via Graph Hypernetworks—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