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 3651–3675 of 4925 papers

TitleStatusHype
Reducing Channel Estimation and Feedback Overhead in IRS-Aided Downlink System: A Quantize-then-Estimate Approach—0
Quantum Block-Matching Algorithm using Dissimilarity Measure—0
AQUILA: Communication Efficient Federated Learning with Adaptive Quantization in Device Selection Strategy—0
Arabic Compact Language Modelling for Resource Limited Devices—0
Arbitrary Bit-width Network: A Joint Layer-Wise Quantization and Adaptive Inference Approach—0
A Reconfigurable Dual-Mode Tracking SAR ADC without Analog Subtraction—0
A reconfigurable neural network ASIC for detector front-end data compression at the HL-LHC—0
A Reconstruction-Computation-Quantization (RCQ) Approach to Node Operations in LDPC Decoding—0
Are Conventional SNNs Really Efficient? A Perspective from Network Quantization—0
Are disentangled representations all you need to build speaker anonymization systems?—0
High-performance deep spiking neural networks with 0.3 spikes per neuron—0
A review of learning vector quantization classifiers—0
A Review of Recent Advances of Binary Neural Networks for Edge Computing—0
Are Words the Quanta of Human Language? Extending the Domain of Quantum Cognition—0
A Rigorous Analysis of Least Squares Sine Fitting Using Quantized Data: the Random Phase Case—0
ARM 4-BIT PQ: SIMD-based Acceleration for Approximate Nearest Neighbor Search on ARM—0
A Robust and Low Complexity Deep Learning Model for Remote Sensing Image Classification—0
A Robust Deep Learning-Based Beamforming Design for RIS-assisted Multiuser MISO Communications with Practical Constraints—0
A Robust Visual Sampling Model Inspired by Receptive Field—0
ARQ: A Mixed-Precision Quantization Framework for Accurate and Certifiably Robust DNNs—0
Art and Science of Quantizing Large-Scale Models: A Comprehensive Overview—0
Artificial neural networks condensation: A strategy to facilitate adaption of machine learning in medical settings by reducing computational burden—0
A Safe Self-evolution Algorithm for Autonomous Driving Based on Data-Driven Risk Quantification Model—0
A-SDM: Accelerating Stable Diffusion through Redundancy Removal and Performance Optimization—0
A Secure Federated Learning Framework for Residential Short Term Load Forecasting—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