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 2451–2500 of 4925 papers

TitleStatusHype
Quantization in Relative Gradient Angle Domain For Building Polygon Estimation—0
Quantization Loss Re-Learning Method—0
Quantization Meets Reasoning: Exploring LLM Low-Bit Quantization Degradation for Mathematical Reasoning—0
Quantization Mimic: Towards Very Tiny CNN for Object Detection—0
Quantization of Acoustic Model Parameters in Automatic Speech Recognition Framework—0
Quantifying Climate Change Impacts on Renewable Energy Generation: A Super-Resolution Recurrent Diffusion Model—0
Quantization of Deep Neural Networks for Accumulator-constrained Processors—0
Quantization of Deep Neural Networks for Accurate Edge Computing—0
Quantization of Deep Neural Networks to facilitate self-correction of weights on Phase Change Memory-based analog hardware—0
Quantization of Fully Convolutional Networks for Accurate Biomedical Image Segmentation—0
Quantization of Generative Adversarial Networks for Efficient Inference: a Methodological Study—0
Quantization of Large Language Models with an Overdetermined Basis—0
Quantization optimized with respect to the Haar basis—0
Quantization Robust Federated Learning for Efficient Inference on Heterogeneous Devices—0
Quantization Under the Real-world Measure: Fast and Accurate Valuation of Long-dated Contracts—0
Quantized Adam with Error Feedback—0
Quantized Adaptive Subgradient Algorithms and Their Applications—0
Quantized and Asynchronous Federated Learning—0
Quantized and Interpretable Learning Scheme for Deep Neural Networks in Classification Task—0
Quantized Approximately Orthogonal Recurrent Neural Networks—0
Quantized Compressive K-Means—0
Quantized Consensus under Data-Rate Constraints and DoS Attacks: A Zooming-In and Holding Approach—0
Quantized Context Based LIF Neurons for Recurrent Spiking Neural Networks in 45nm—0
Quantized control of non-Lipschitz nonlinear systems: a novel control framework with prescribed transient performance and lower design complexity—0
Quantized Convolutional Neural Networks Through the Lens of Partial Differential Equations—0
Quantized Decoder in Learned Image Compression for Deterministic Reconstruction—0
Quantized Deep Path-following Control on a Microcontroller—0
Quantized Delta Weight Is Safety Keeper—0
Quantized Dissipative Uncertain Model for Fractional T_S Fuzzy systems with Time_Varying Delays Under Networked Control System—0
Quantized distributed Nash equilibrium seeking under DoS attacks—0
Quantized Distributed Training of Large Models with Convergence Guarantees—0
Quantized Embedding Vectors for Controllable Diffusion Language Models—0
Quantized Epoch-SGD for Communication-Efficient Distributed Learning—0
Quantized Feature Distillation for Network Quantization—0
Quantized Federated Learning under Transmission Delay and Outage Constraints—0
Quantized Frank-Wolfe: Faster Optimization, Lower Communication, and Projection Free—0
Quantized Guided Pruning for Efficient Hardware Implementations of Convolutional Neural Networks—0
A Hierarchical Federated Learning Approach for the Internet of Things—0
Quantized Kernel Learning for Feature Matching—0
Quantized Low-Rank Multivariate Regression with Random Dithering—0
Quantized Memory-Augmented Neural Networks—0
Quantized Minimum Error Entropy Criterion—0
Quantized neural network design under weight capacity constraint—0
Quantized neural network for complex hologram generation—0
Quantized Neural Network Inference with Precision Batching—0
Quantized Neural Networks: Characterization and Holistic Optimization—0
Quantized Neural Networks for Low-Precision Accumulation with Guaranteed Overflow Avoidance—0
Quantized Neural Networks for Radar Interference Mitigation—0
Quantized Nonparametric Estimation over Sobolev Ellipsoids—0
Quantized Precoding and RIS-Assisted Modulation for Integrated Sensing and Communications Systems—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