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 1901–1925 of 4925 papers

TitleStatusHype
FullPack: Full Vector Utilization for Sub-Byte Quantized Inference on General Purpose CPUs—0
Full-Precision Free Binary Graph Neural Networks—0
An End-to-End DNN Inference Framework for the SpiNNaker2 Neuromorphic MPSoC—0
Fully Digital Second-order Level-crossing Sampling ADC for Data Saving in Sensing Sparse Signals—0
Fully Distributed and Quantized Algorithm for MPC-based Autonomous Vehicle Platooning Optimization—0
Homomorphic Encryption-Enabled Distance-Based Distributed Formation Control with Distance Mismatch Estimators—0
Downlink Clustering-Based Scheduling of IRS-Assisted Communications With Reconfiguration Constraints—0
Fully Quantized Network for Object Detection—0
Double Viterbi: Weight Encoding for High Compression Ratio and Fast On-Chip Reconstruction for Deep Neural Network—0
Functional Invariants to Watermark Large Transformers—0
Functional quantization of rough volatility and applications to volatility derivatives—0
Fundamental Limits of Communication Efficiency for Model Aggregation in Distributed Learning: A Rate-Distortion Approach—0
Fundamental Trade-offs in Quantized Hybrid Radar Fusion: A CRB-Rate Perspective—0
FunQuant: A R package to perform quantization in the context of rare events and time-consuming simulations—0
FusionSAM: Latent Space driven Segment Anything Model for Multimodal Fusion and Segmentation—0
Fuzzy-Based Dialectical Non-Supervised Image Classification and Clustering—0
Fuzzy Norm-Explicit Product Quantization for Recommender Systems—0
FxP-QNet: A Post-Training Quantizer for the Design of Mixed Low-Precision DNNs with Dynamic Fixed-Point Representation—0
Double Quantization for Communication-Efficient Distributed Optimization—0
Double JPEG Detection in Mixed JPEG Quality Factors using Deep Convolutional Neural Network—0
Bit Efficient Quantization for Deep Neural Networks—0
GANCompress: GAN-Enhanced Neural Image Compression with Binary Spherical Quantization—0
A blob method for inhomogeneous diffusion with applications to multi-agent control and sampling—0
Haar Wavelet Feature Compression for Quantized Graph Convolutional Networks—0
DoTA: Weight-Decomposed Tensor Adaptation for Large Language 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