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 4101–4150 of 4925 papers

TitleStatusHype
AddNet: Deep Neural Networks Using FPGA-Optimized Multipliers—0
CoopNet: Cooperative Convolutional Neural Network for Low-Power MCUsCode0
vqSGD: Vector Quantized Stochastic Gradient Descent—0
Efficient Hardware Implementation of Incremental Learning and Inference on Chip—0
Transductive Zero-Shot Hashing for Multilabel Image RetrievalCode0
Loss Aware Post-training QuantizationCode0
Data Efficient Stagewise Knowledge DistillationCode0
One-Bit Sigma-Delta modulation on the circle—0
The Canonical Distortion Measure for Vector Quantization and Function Approximation—0
DupNet: Towards Very Tiny Quantized CNN with Improved Accuracy for Face Detection—0
What Do Compressed Deep Neural Networks Forget?Code0
Quantization-based Bermudan option pricing in the FX world—0
Hyper-Sphere Quantization: Communication-Efficient SGD for Federated LearningCode0
Norm-Explicit Quantization: Improving Vector Quantization for Maximum Inner Product SearchCode0
Iteratively Training Look-Up Tables for Network Quantization—0
Multiple yield curve modelling with CBI processes—0
A Programmable Approach to Neural Network CompressionCode0
Post-Training 4-bit Quantization on Embedding Tables—0
Ternary MobileNets via Per-Layer Hybrid Filter Banks—0
Comprehensive SNN Compression Using ADMM Optimization and Activity RegularizationCode0
Adaptive Precision Training: Quantify Back Propagation in Neural Networks with Fixed-point Numbers—0
LFZip: Lossy compression of multivariate floating-point time series data via improved predictionCode0
Memory Requirement Reduction of Deep Neural Networks Using Low-bit Quantization of Parameters—0
MulCode: A Multiplicative Multi-way Model for Compressing Neural Language Model—0
On Distributed Quantization for Classification—0
On Neural Architecture Search for Resource-Constrained Hardware Platforms—0
SPARQ-SGD: Event-Triggered and Compressed Communication in Decentralized Stochastic Optimization—0
Channel Estimation for MIMO Hybrid Architectures with Low Resolution ADCs for mmWave Communication—0
Exploiting Intelligent Reflecting Surfaces in NOMA Networks: Joint Beamforming Optimization—0
Training DNN IoT Applications for Deployment On Analog NVM Crossbars—0
Integrating PHY Security Into NDN-IoT Networks By Exploiting MEC: Authentication Efficiency, Robustness, and Accuracy Enhancement—0
Noiseless Privacy—0
Towards Unsupervised Speech Recognition and Synthesis with Quantized Speech Representation Learning—0
Secure Evaluation of Quantized Neural Networks—0
Asynchronous Decentralized SGD with Quantized and Local Updates—0
Q-GADMM: Quantized Group ADMM for Communication Efficient Decentralized Machine Learning—0
CNN-based Analog CSI Feedback in FDD MIMO-OFDM Systems—0
A Binary Variational Autoencoder for HashingCode0
Image processing in DNA—0
Mirror Descent View for Neural Network QuantizationCode0
Fully Quantized Transformer for Machine Translation—0
Reinforced Bit Allocation under Task-Driven Semantic Distortion Metrics—0
Variation-aware Binarized Memristive Networks—0
Parametric context adaptive Laplace distribution for multimedia compression—0
Automatic Neural Network Compression by Sparsity-Quantization Joint Learning: A Constrained Optimization-based ApproachCode0
OverQ: Opportunistic Outlier Quantization for Neural Network Accelerators—0
High-Dimensional Stochastic Gradient Quantization for Communication-Efficient Edge Learning—0
QPyTorch: A Low-Precision Arithmetic Simulation FrameworkCode0
Bit Efficient Quantization for Deep Neural Networks—0
REMIND Your Neural Network to Prevent Catastrophic ForgettingCode0
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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