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 3251–3300 of 4925 papers

TitleStatusHype
On the Acceleration of Deep Neural Network Inference using Quantized Compressed Sensing—0
Rate distortion comparison of a few gradient quantizers—0
Reconfigurable co-processor architecture with limited numerical precision to accelerate deep convolutional neural networks—0
Integer-arithmetic-only Certified Robustness for Quantized Neural Networks—0
Quantization Backdoors to Deep Learning Commercial Frameworks—0
Verifying Low-dimensional Input Neural Networks via Input Quantization—0
Deployment of Deep Neural Networks for Object Detection on Edge AI Devices with Runtime Optimization—0
Distance-aware Quantization—0
FOX-NAS: Fast, On-device and Explainable Neural Architecture SearchCode1
Pruning vs XNOR-Net: A Comprehensive Study of Deep Learning for Audio Classification on Edge-devicesCode0
Audio Spectral Enhancement: Leveraging Autoencoders for Low Latency Reconstruction of Long, Lossy Audio SequencesCode0
Energy Efficiency Maximization Precoding for Quantized Massive MIMO Systems—0
Generalizable Mixed-Precision Quantization via Attribution Rank PreservationCode1
Bifocal Neural ASR: Exploiting Keyword Spotting for Inference Optimization—0
MOHAQ: Multi-Objective Hardware-Aware Quantization of Recurrent Neural Networks—0
All-Digital LoS MIMO with Low-Precision Analog-to-Digital Conversion—0
Communication-Efficient Federated Learning via Predictive CodingCode0
Jointly Optimizing Query Encoder and Product Quantization to Improve Retrieval PerformanceCode1
DQ-SGD: Dynamic Quantization in SGD for Communication-Efficient Distributed Learning—0
Connecting Compression Spaces with Transformer for Approximate Nearest Neighbor Search—0
Local Morphometry of Closed, Implicit Surfaces—0
Sparse Joint Transmission for Cloud Radio Access Networks with Limited Fronthaul Capacity—0
QuPeD: Quantized Personalization via Distillation with Applications to Federated Learning—0
Adaptive Precision Training (AdaPT): A dynamic fixed point quantized training approach for DNNs—0
Uniformity in Heterogeneity:Diving Deep into Count Interval Partition for Crowd CountingCode1
DV-Det: Efficient 3D Point Cloud Object Detection with Dynamic Voxelization—0
High-Dimensional Distribution Generation Through Deep Neural Networks—0
HYPER-SNN: Towards Energy-efficient Quantized Deep Spiking Neural Networks for Hyperspectral Image Classification—0
Deep Learning for Estimation and Pilot Signal Design in Few-Bit Massive MIMO Systems—0
Finite-Bit Quantization For Distributed Algorithms With Linear Convergence—0
Pruning Ternary Quantization—0
Kramers-Kronig Receiver Combined With Digital Resolution Enhancer—0
HARP-Net: Hyper-Autoencoded Reconstruction Propagation for Scalable Neural Audio Coding—0
CREW: Computation Reuse and Efficient Weight Storage for Hardware-accelerated MLPs and RNNs—0
Support Recovery in Universal One-bit Compressed Sensing—0
DHNet: Double MPEG-4 Compression Detection via Multiple DCT Histograms—0
A High-Performance Adaptive Quantization Approach for Edge CNN Applications—0
Deep Learning to Ternary Hash Codes by Continuation—0
Continuous-variable neural-network quantum states and the quantum rotor modelCode0
MAFAT: Memory-Aware Fusing and Tiling of Neural Networks for Accelerated Edge Inference—0
Efficient Approximate Search for Sets of Vectors—0
Regularized Classification-Aware QuantizationCode0
LANA: Latency Aware Network Acceleration—0
HEMP: High-order Entropy Minimization for neural network comPression—0
Model compression as constrained optimization, with application to neural nets. Part V: combining compressions—0
Patch-Wise Spatial-Temporal Quality Enhancement for HEVC Compressed VideoCode0
An Embedded Iris Recognition System Optimization using Dynamically ReconfigurableDecoder with LDPC Codes—0
Image restoration quality assessment based on regional differential information entropy—0
SimCC: a Simple Coordinate Classification Perspective for Human Pose EstimationCode1
Deep Learning Methods for Joint Optimization of Beamforming and Fronthaul Quantization in Cloud Radio Access Networks—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