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 3851–3900 of 4925 papers

TitleStatusHype
Knowledge Distillation-aided End-to-End Learning for Linear Precoding in Multiuser MIMO Downlink Systems with Finite-Rate Feedback—0
Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physics—0
In-Depth DCT Coefficient Distribution Analysis for First Quantization Estimation—0
Subjective Quality Database and Objective Study of Compressed Point Clouds With 6DoF Head-Mounted Display—0
Fast top-K Cosine Similarity Search through XOR-Friendly Binary Quantization on GPUs—0
TREND: Transferability based Robust ENsemble DesignCode0
A Novel Physics-based Channel Model for Reconfigurable Intelligent Surface-assisted Multi-user Communication Systems—0
Cluster-Based Cooperative Digital Over-the-Air Aggregation for Wireless Federated Edge Learning—0
The Rate-Distortion-Accuracy Tradeoff: JPEG Case Study—0
Super-relaxation of space-time-quantized ensemble of energy loads to curtail their synchronization after demand response perturbation—0
Uplink Achievable Rate of Intelligent Reflecting Surface-Aided Millimeter-Wave Communications with Low-Resolution ADC and Phase Noise—0
Deep Multi-modality Soft-decoding of Very Low Bit-rate Face Videos—0
High-quality Single-model Deep Video Compression with Frame-Conv3D and Multi-frame Differential Modulation—0
FTL: A universal framework for training low-bit DNNs via Feature Transfer—0
ProxyBNN: Learning Binarized Neural Networks via Proxy Matrices—0
Task-Aware Quantization Network for JPEG Image Compression—0
Binarized Neural Network for Single Image Super Resolution—0
Automatic Gain Control Design for Dynamic Visible Light Communication Systems—0
Communication-Efficient Federated Learning via Optimal Client Sampling—0
Flexible framework for audio reconstructionCode0
On the Impact of Lossy Image and Video Compression on the Performance of Deep Convolutional Neural Network Architectures—0
WrapNet: Neural Net Inference with Ultra-Low-Resolution Arithmetic—0
End-to-end Learning of Compressible Features—0
A Post-coder Feedback Approach to Overcome Training Asymmetry in MIMO-TDD—0
Analysis and Optimization for RIS-Aided Multi-Pair Communications Relying on Statistical CSI—0
Byzantine-Resilient Secure Federated Learning—0
Search What You Want: Barrier Panelty NAS for Mixed Precision Quantization—0
Differentiable Joint Pruning and Quantization for Hardware Efficiency—0
DBQ: A Differentiable Branch Quantizer for Lightweight Deep Neural Networks—0
Exploiting vulnerabilities of deep neural networks for privacy protectionCode0
Training with reduced precision of a support vector machine model for text classification—0
FSpiNN: An Optimization Framework for Memory- and Energy-Efficient Spiking Neural Networks—0
eSampling: Energy Harvesting ADCs—0
Compression strategies and space-conscious representations for deep neural networks—0
A General Family of Stochastic Proximal Gradient Methods for Deep Learning—0
Finding Non-Uniform Quantization Schemes using Multi-Task Gaussian ProcessesCode0
Image De-Quantization Using Generative Models as Priors—0
Adaptive Periodic Averaging: A Practical Approach to Reducing Communication in Distributed Learning—0
Experimental results on palmvein-based personal recognition by multi-snapshot fusion of textural features—0
Term Revealing: Furthering Quantization at Run Time on Quantized DNNs—0
Quantization in Relative Gradient Angle Domain For Building Polygon Estimation—0
SGQuant: Squeezing the Last Bit on Graph Neural Networks with Specialized Quantization—0
AUSN: Approximately Uniform Quantization by Adaptively Superimposing Non-uniform Distribution for Deep Neural Networks—0
Enabling On-Device CNN Training by Self-Supervised Instance Filtering and Error Map Pruning—0
Learning with tree tensor networks: complexity estimates and model selection—0
Hardware Acceleration of Sparse and Irregular Tensor Computations of ML Models: A Survey and Insights—0
Compressing Neural Machine Translation Models with 4-bit Precision—0
Edinburgh's Submissions to the 2020 Machine Translation Efficiency Task—0
Learning to Quantize Deep Neural Networks: A Competitive-Collaborative Approach—0
Robust Product Markovian Quantization—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