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

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Showing 3701–3750 of 4925 papers

TitleStatusHype
On Round-Off Errors and Gaussian Blur in Superresolution and in Image Registration—0
On the Acceleration of Deep Neural Network Inference using Quantized Compressed Sensing—0
On the Adversarial Robustness of Quantized Neural Networks—0
On the Compressibility of Quantized Large Language Models—0
On the Compression of Language Models for Code: An Empirical Study on CodeBERT—0
On the Distribution, Sparsity, and Inference-time Quantization of Attention Values in Transformers—0
On the effectiveness of discrete representations in sparse mixture of experts—0
On the Effectiveness of Hybrid Mutual Information Estimation—0
On the Effect of Quantization on Dynamic Mode Decomposition—0
On the efficient representation and execution of deep acoustic models—0
On the fly Deep Neural Network Optimization Control for Low-Power Computer Vision—0
On the Impact of Fixed Point Hardware for Optical Fiber Nonlinearity Compensation Algorithms—0
On the Impact of Lossy Image and Video Compression on the Performance of Deep Convolutional Neural Network Architectures—0
On the Impact of Quantization and Pruning of Self-Supervised Speech Models for Downstream Speech Recognition Tasks "In-the-Wild''—0
On the Interplay of Privacy, Persuasion and Quantization—0
On the Logic Elements Associated with Round-Off Errors and Gaussian Blur in Image Registration: A Simple Case of Commingling—0
On the Needs for Rotations in Hypercubic Quantization Hashing—0
On the Orthogonality of Knowledge Distillation with Other Techniques: From an Ensemble Perspective—0
On the Pareto Efficiency of Quantized CNN—0
On the Privacy-Preserving Properties of Spiking Neural Networks with Unique Surrogate Gradients and Quantization Levels—0
On the Quantization of Cellular Neural Networks for Cyber-Physical Systems—0
On the quantization of recurrent neural networks—0
On the Relation Between Speech Quality and Quantized Latent Representations of Neural Codecs—0
On the relevance of language in speaker recognition—0
On the Role of Spatial Effects in Early Estimates of Disease Infectiousness: A Second Quantization Approach—0
On the Tradeoff between Energy, Precision, and Accuracy in Federated Quantized Neural Networks—0
On the Universal Approximability and Complexity Bounds of Quantized ReLU Neural Networks—0
On the Uplink Achievable Rate of Massive MIMO System With Low-Resolution ADC and RF Impairments—0
On Uniform Scalar Quantization for Learned Image Compression—0
OPAL: Outlier-Preserved Microscaling Quantization Accelerator for Generative Large Language Models—0
OpenDPDv2: A Unified Learning and Optimization Framework for Neural Network Digital Predistortion—0
OpenNMT System Description for WNMT 2018: 800 words/sec on a single-core CPU—0
Operation-guided Neural Networks for High Fidelity Data-To-Text Generation—0
OPQ: Compressing Deep Neural Networks with One-shot Pruning-Quantization—0
OptComNet: Optimized Neural Networks for Low-Complexity Channel Estimation—0
Optical Transformers—0
Optimal and Near-Optimal Adaptive Vector Quantization—0
OPTIMAL BINARY QUANTIZATION FOR DEEP NEURAL NETWORKS—0
Optimal Controller and Quantizer Selection for Partially Observable Linear-Quadratic-Gaussian Systems—0
Optimal Controller Synthesis and Dynamic Quantizer Switching for Linear-Quadratic-Gaussian Systems—0
Optimal Database Allocation in Finite Time with Efficient Communication and Transmission Stopping over Dynamic Networks—0
Optimal Gradient Compression for Distributed and Federated Learning—0
Optimal Gradient Quantization Condition for Communication-Efficient Distributed Training—0
Nonparametric Inference under B-bits Quantization—0
Optimal Privacy Preserving for Federated Learning in Mobile Edge Computing—0
Optimal Quantization for Batch Normalization in Neural Network Deployments and Beyond—0
Optimal Quantization Using Scaled Codebook—0
Optimising TinyML with Quantization and Distillation of Transformer and Mamba Models for Indoor Localisation on Edge Devices—0
Optimization and Deployment of Deep Neural Networks for PPG-based Blood Pressure Estimation Targeting Low-power Wearables—0
Optimization of DNN-based speaker verification model through efficient quantization technique—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