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

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Showing 1701–1750 of 4925 papers

TitleStatusHype
Dynamic Signal Measurements Based on Quantized Data—0
Communication-Efficient Federated Distillation—0
Fast Autoregressive Models for Continuous Latent Generation—0
Fast binary embeddings, and quantized compressed sensing with structured matrices—0
Dynamic quantized consensus under DoS attacks: Towards a tight zooming-out factor—0
Fast, Compact, and High Quality LSTM-RNN Based Statistical Parametric Speech Synthesizers for Mobile Devices—0
Dynamic Quantized Consensus of General Linear Multi-agent Systems under Denial-of-Service Attacks—0
Blind-Adaptive Quantizers—0
Fast DistilBERT on CPUs—0
FAST: DNN Training Under Variable Precision Block Floating Point with Stochastic Rounding—0
Fastening the Initial Access in 5G NR Sidelink for 6G V2X Networks—0
Arabic Compact Language Modelling for Resource Limited Devices—0
Faster Inference of Integer SWIN Transformer by Removing the GELU Activation—0
Faster Neural Net Inference via Forests of Sparse Oblique Decision Trees—0
Communication-efficient k-Means for Edge-based Machine Learning—0
FastICARL: Fast Incremental Classifier and Representation Learning with Efficient Budget Allocation in Audio Sensing Applications—0
Fast Implementation of 4-bit Convolutional Neural Networks for Mobile Devices—0
An Extra RMSNorm is All You Need for Fine Tuning to 1.58 Bits—0
Dynamic Q&A of Clinical Documents with Large Language Models—0
Fast Jet Tagging with MLP-Mixers on FPGAs—0
Fast Large-Scale Discrete Optimization Based on Principal Coordinate Descent—0
Fast learning rates with heavy-tailed losses—0
Dynamic Probabilistic Pruning: A general framework for hardware-constrained pruning at different granularities—0
Blending Low and High-Level Semantics of Time Series for Better Masked Time Series Generation—0
FastMamba: A High-Speed and Efficient Mamba Accelerator on FPGA with Accurate Quantization—0
Dynamic Predictive Sampling Analog to Digital Converter for Sparse Signal Sensing—0
Blended Coarse Gradient Descent for Full Quantization of Deep Neural Networks—0
ADaPTION: Toolbox and Benchmark for Training Convolutional Neural Networks with Reduced Numerical Precision Weights and Activation—0
FQ-Conv: Fully Quantized Convolution for Efficient and Accurate Inference—0
Adaptive Error-Bounded Hierarchical Matrices for Efficient Neural Network Compression—0
Bi-ViT: Pushing the Limit of Vision Transformer Quantization—0
FastSGD: A Fast Compressed SGD Framework for Distributed Machine Learning—0
Dynamic Cell Modeling of Li-Ion Polymer Batteries for Precise SOC Estimation in Power-Needy Autonomous Electric Vehicles—0
DyBit: Dynamic Bit-Precision Numbers for Efficient Quantized Neural Network Inference—0
An Experimental Study of Reduced-Voltage Operation in Modern FPGAs for Neural Network Acceleration—0
DV-Det: Efficient 3D Point Cloud Object Detection with Dynamic Voxelization—0
Fast top-K Cosine Similarity Search through XOR-Friendly Binary Quantization on GPUs—0
FAT: An In-Memory Accelerator with Fast Addition for Ternary Weight Neural Networks—0
DupNet: Towards Very Tiny Quantized CNN with Improved Accuracy for Face Detection—0
FATNN: Fast and Accurate Ternary Neural Networks—0
Fault-Tolerant Four-Dimensional Constellation for Coherent Optical Transmission Systems—0
BitTTS: Highly Compact Text-to-Speech Using 1.58-bit Quantization and Weight Indexing—0
FBI: Fingerprinting models with Benign Inputs—0
Compact recurrent neural networks for acoustic event detection on low-energy low-complexity platforms—0
FBQuant: FeedBack Quantization for Large Language Models—0
FCN-Pose: A Pruned and Quantized CNN for Robot Pose Estimation for Constrained Devices—0
An Experimental Study: Assessing the Combined Framework of WavLM and BEST-RQ for Text-to-Speech Synthesis—0
FD Cell-Free mMIMO: Analysis and Optimization—0
Dual Precision Quantization for Efficient and Accurate Deep Neural Networks Inference—0
An Exact Quantized Decentralized Gradient Descent Algorithm—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