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 2826–2850 of 4925 papers

TitleStatusHype
Injecting Domain Adaptation with Learning-to-hash for Effective and Efficient Zero-shot Dense RetrievalCode1
Nonparametric Decentralized Detection and Sparse Sensor Selection via Multi-Sensor Online Kernel Scalar Quantization—0
QADAM: Quantization-Aware DNN Accelerator Modeling for Pareto-Optimality—0
Energy-efficient Deployment of Deep Learning Applications on Cortex-M based Microcontrollers using Deep Compression—0
Approximate Message Passing with Parameter Estimation for Heavily Quantized MeasurementsCode0
Service Delay Minimization for Federated Learning over Mobile Devices—0
Positional Information is All You Need: A Novel Pipeline for Self-Supervised SVDE from Videos—0
A Silicon Photonic Accelerator for Convolutional Neural Networks with Heterogeneous Quantization—0
Towards Robust Low Light Image Enhancement—0
QAPPA: Quantization-Aware Power, Performance, and Area Modeling of DNN Accelerators—0
SQ-VAE: Variational Bayes on Discrete Representation with Self-annealed Stochastic QuantizationCode1
Real-time semantic segmentation on FPGAs for autonomous vehicles with hls4ml—0
Towards Lossless ANN-SNN Conversion under Ultra-Low Latency with Dual-Phase OptimizationCode0
A Comprehensive Survey on Model Quantization for Deep Neural Networks in Image Classification—0
Tighter Regret Analysis and Optimization of Online Federated Learning—0
VQFR: Blind Face Restoration with Vector-Quantized Dictionary and Parallel DecoderCode2
Adaptive Block Floating-Point for Analog Deep Learning Hardware—0
Neural Network-based OFDM Receiver for Resource Constrained IoT Devices—0
Serving and Optimizing Machine Learning Workflows on Heterogeneous Infrastructures—0
A 14uJ/Decision Keyword Spotting Accelerator with In-SRAM-Computing and On Chip Learning for Customization—0
Neuromimetic Linear Systems -- Resilience and Learning—0
Reduce Information Loss in Transformers for Pluralistic Image InpaintingCode1
Protecting Data from all Parties: Combining FHE and DP in Federated Learning—0
Block Modulating Video Compression: An Ultra Low Complexity Image Compression Encoder for Resource Limited Platforms—0
Online Model Compression for Federated Learning with Large Models—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