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 3501–3550 of 4925 papers

TitleStatusHype
An Implementation of Vector Quantization using the Genetic Algorithm Approach—0
FAT: Learning Low-Bitwidth Parametric Representation via Frequency-Aware TransformationCode1
The Wavefunction of Continuous-Time Recurrent Neural Networks—0
Confounding Tradeoffs for Neural Network QuantizationCode1
The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure AggregationCode0
Visualizing hierarchies in scRNA-seq data using a density tree-biased autoencoderCode0
Group Quantization of Quadratic Hamiltonians in Finance—0
BRECQ: Pushing the Limit of Post-Training Quantization by Block ReconstructionCode1
Distribution Adaptive INT8 Quantization for Training CNNs—0
On the Universal Transformation of Data-Driven Models to Control SystemsCode1
Sparsification via Compressed Sensing for Automatic Speech Recognition—0
Enabling Binary Neural Network Training on the EdgeCode1
VS-Quant: Per-vector Scaled Quantization for Accurate Low-Precision Neural Network Inference—0
Communication-efficient k-Means for Edge-based Machine Learning—0
Adaptive Quantization of Model Updates for Communication-Efficient Federated Learning—0
Refining a -nearest neighbor graph for a computationally efficient spectral clusteringCode0
Symbolic Models for Infinite Networks of Control Systems: A Compositional Approach—0
Compressed Object DetectionCode0
Low Bit-Rate Wideband Speech Coding: A Deep Generative Model based Approach—0
Progressive Neural Image Compression with Nested Quantization and Latent Ordering—0
Fixed-point Quantization of Convolutional Neural Networks for Quantized Inference on Embedded PlatformsCode1
Image Splicing Detection, Localization and Attribution via JPEG Primary Quantization Matrix Estimation and Clustering—0
FEDZIP: A Compression Framework for Communication-Efficient Federated LearningCode0
Benchmarking Quantized Neural Networks on FPGAs with FINNCode1
Probabilistic Learning Vector Quantization on Manifold of Symmetric Positive Definite Matrices—0
Rescuing Deep Hashing from Dead Bits Problem—0
Understanding Cache Boundness of ML Operators on ARM ProcessorsCode0
CAMBI: Contrast-aware Multiscale Banding Index—0
Performance of Cell-Free MmWave Massive MIMO Systems with Fronthaul Compression and DAC Quantization—0
AdderNet and its Minimalist Hardware Design for Energy-Efficient Artificial Intelligence—0
Pruning and Quantization for Deep Neural Network Acceleration: A Survey—0
Error Diffusion Halftoning Against Adversarial ExamplesCode0
Continual Learning of Generative Models with Limited Data: From Wasserstein-1 Barycenter to Adaptive Coalescence—0
Generative Zero-shot Network Quantization—0
Overfitting for Fun and Profit: Instance-Adaptive Data Compression—0
Time-Correlated Sparsification for Communication-Efficient Federated Learning—0
SparseDNN: Fast Sparse Deep Learning Inference on CPUsCode1
ES-ENAS: Efficient Evolutionary Optimization for Large Hybrid Search SpacesCode0
Multi-Task Network Pruning and Embedded Optimization for Real-time Deployment in ADAS—0
Deep Compression of Neural Networks for Fault Detection on Tennessee Eastman Chemical Processes—0
KDLSQ-BERT: A Quantized Bert Combining Knowledge Distillation with Learned Step Size Quantization—0
On the quantization of recurrent neural networks—0
FBGEMM: Enabling High-Performance Low-Precision Deep Learning InferenceCode2
Energy-Efficient Distributed Learning Algorithms for Coarsely Quantized Signals—0
Fast convolutional neural networks on FPGAs with hls4mlCode2
Towards Energy Efficient Federated Learning over 5G+ Mobile Devices—0
Single-path Bit Sharing for Automatic Loss-aware Model Compression—0
Binary TTC: A Temporal Geofence for Autonomous NavigationCode1
Sound Event Detection with Binary Neural Networks on Tightly Power-Constrained IoT Devices—0
Activation Density based Mixed-Precision Quantization for Energy Efficient Neural 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