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 3801–3850 of 4925 papers

TitleStatusHype
Stochastic Precision Ensemble: Self-Knowledge Distillation for Quantized Deep Neural Networks—0
BAMSProd: A Step towards Generalizing the Adaptive Optimization Methods to Deep Binary Model—0
Recursive CSI Quantization of Time-Correlated MIMO Channels by Deep Learning ClassificationCode0
On Efficient Constructions of Checkpoints—0
Once Quantized for All: Progressively Searching for Quantized Compact Models—0
TernaryBERT: Distillation-aware Ultra-low Bit BERTCode0
Perceptual Video Quality Prediction Emphasizing Chroma Distortions—0
A Markovian Model-Driven Deep Learning Framework for Massive MIMO CSI Feedback—0
Probabilistically Sampled and Spectrally Clustered Plant Genotypes using Phenotypic Characteristics—0
Resource Allocation and Dithering of Bayesian Parameter Estimation Using Mixed-Resolution Data—0
Towards Fully 8-bit Integer Inference for the Transformer Model—0
Extremely Low Bit Transformer Quantization for On-Device Neural Machine Translation—0
MSP: An FPGA-Specific Mixed-Scheme, Multi-Precision Deep Neural Network Quantization Framework—0
Approximate spectral clustering using both reference vectors and topology of the network generated by growing neural gasCode0
Fast Implementation of 4-bit Convolutional Neural Networks for Mobile Devices—0
Communication Efficient Distributed Learning with Censored, Quantized, and Generalized Group ADMM—0
QuantNet: Learning to Quantize by Learning within Fully Differentiable Framework—0
On the Orthogonality of Knowledge Distillation with Other Techniques: From an Ensemble Perspective—0
FleXOR: Trainable Fractional Quantization—0
Alternating Direction Method of Multipliers for Quantization—0
ECG Beats Fast Classification Base on Sparse DictionariesCode0
Algorithm and VLSI Design for 1-bit Data Detection in Massive MIMO-OFDMCode0
Deep data compression for approximate ultrasonic image formation—0
Layer-specific Optimization for Mixed Data Flow with Mixed Precision in FPGA Design for CNN-based Object Detectors—0
Transform Quantization for CNN (Convolutional Neural Network) Compression—0
A new heuristic algorithm for fast k-segmentation—0
Scaling Up Deep Neural Network Optimization for Edge Inference—0
Object Detection-Based Variable Quantization Processing—0
An Integrated Approach to Produce Robust Models with High EfficiencyCode0
Optimal Quantization for Batch Normalization in Neural Network Deployments and Beyond—0
An adaptive random experiment design method for engineering experiment—0
Stochastic Markov Gradient Descent and Training Low-Bit Neural Networks—0
Convergence of Federated Learning over a Noisy Downlink—0
Stochastic Hybrid Combining Design for Quantized Massive MIMO Systems—0
Lossy Image Compression with Normalizing Flows—0
One Weight Bitwidth to Rule Them All—0
Utilizing Explainable AI for Quantization and Pruning of Deep Neural Networks—0
Training of mixed-signal optical convolutional neural network with reduced quantization level—0
Channel-wise Hessian Aware trace-Weighted Quantization of Neural Networks—0
False Detection (Positives and Negatives) in Object Detection—0
ECG beats classification via online sparse dictionary and time pyramid matchingCode0
Weight Equalizing Shift Scaler-Coupled Post-training Quantization—0
FATNN: Fast and Accurate Ternary Neural Networks—0
Leveraging Automated Mixed-Low-Precision Quantization for tiny edge microcontrollers—0
Compression of Deep Learning Models for Text: A Survey—0
Degree-Quant: Quantization-Aware Training for Graph Neural Networks—0
Hardware-Centric AutoML for Mixed-Precision Quantization—0
Unsupervised Learning For Sequence-to-sequence Text-to-speech For Low-resource Languages—0
End-to-End Rate-Distortion Optimization for Bi-Directional Learned Video Compression—0
The Sockeye 2 Neural Machine Translation Toolkit at AMTA 2020—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