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 3201–3250 of 4925 papers

TitleStatusHype
To Compress, or Not to Compress: Characterizing Deep Learning Model Compression for Embedded Inference—0
ToneUnit: A Speech Discretization Approach for Tonal Language Speech Synthesis—0
Topological Analysis for Detecting Anomalies (TADA) in Time Series—0
Topologically Controlled Lossy Compression—0
Energy Efficient Federated Learning over Heterogeneous Mobile Devices via Joint Design of Weight Quantization and Wireless Transmission—0
Toward a Diffusion-Based Generalist for Dense Vision Tasks—0
Toward Compact Parameter Representations for Architecture-Agnostic Neural Network Compression—0
Toward Efficient Deep Spiking Neuron Networks:A Survey On Compression—0
Toward Efficient Low-Precision Training: Data Format Optimization and Hysteresis Quantization—0
Toward Extremely Low Bit and Lossless Accuracy in DNNs with Progressive ADMM—0
Toward Fast and Accurate Neural Chinese Word Segmentation with Multi-Criteria Learning—0
Toward INT4 Fixed-Point Training via Exploring Quantization Error for Gradients—0
Toward Joint Image Generation and Compression using Generative Adversarial Networks—0
Toward nonlinear dynamic control over encrypted data for infinite time horizon—0
Towards Accurate and Efficient Sub-8-Bit Integer Training—0
Towards Accurate and High-Speed Spiking Neuromorphic Systems with Data Quantization-Aware Deep Networks—0
Towards Accurate Post-Training Quantization for Vision Transformer—0
Towards Accurate Quantization and Pruning via Data-free Knowledge Transfer—0
Towards AI-Native Fronthaul: Neural Compression for NextG Cloud RAN—0
Towards a tailored mixed-precision sub-8-bit quantization scheme for Gated Recurrent Units using Genetic Algorithms—0
Towards Cheaper Inference in Deep Networks with Lower Bit-Width Accumulators—0
Towards Clip-Free Quantized Super-Resolution Networks: How to Tame Representative Images—0
Towards Compact and Fast Neural Machine Translation Using a Combined Method—0
Towards Effective 2-bit Quantization: Pareto-optimal Bit Allocation for Deep CNNs Compression—0
Towards efficient deep autoencoders for multivariate time series anomaly detection—0
Towards Efficient Full 8-bit Integer DNN Online Training on Resource-limited Devices without Batch Normalization—0
Towards Efficient In-memory Computing Hardware for Quantized Neural Networks: State-of-the-art, Open Challenges and Perspectives—0
Towards Efficient Post-training Quantization of Pre-trained Language Models—0
Towards Efficient Pre-training: Exploring FP4 Precision in Large Language Models—0
Towards Energy-Efficient and Secure Edge AI: A Cross-Layer Framework—0
Towards Energy Efficient Federated Learning over 5G+ Mobile Devices—0
HYPER-SNN: Towards Energy-efficient Quantized Deep Spiking Neural Networks for Hyperspectral Image Classification—0
Towards Fast and Accurate Neural Chinese Word Segmentation with Multi-Criteria Learning—0
Towards Fully 8-bit Integer Inference for the Transformer Model—0
Towards Hardware-Specific Automatic Compression of Neural Networks—0
Towards Improved Text-Aligned Codebook Learning: Multi-Hierarchical Codebook-Text Alignment with Long Text—0
Towards Intelligent Millimeter and Terahertz Communication for 6G: Computer Vision-aided Beamforming—0
Towards Learning Discrete Representations via Self-Supervision for Wearables-Based Human Activity Recognition—0
Towards Lightweight Speaker Verification via Adaptive Neural Network Quantization—0
Towards Low-bit Communication for Tensor Parallel LLM Inference—0
Exploring the Potential of Low-bit Training of Convolutional Neural Networks—0
Towards Low-loss 1-bit Quantization of User-item Representations for Top-K Recommendation—0
Towards Mixed-Precision Quantization of Neural Networks via Constrained Optimization—0
Model-Free Learning for the Linear Quadratic Regulator over Rate-Limited Channels—0
Towards Neural Variational Monte Carlo That Scales Linearly with System Size—0
Towards Next-Level Post-Training Quantization of Hyper-Scale Transformers—0
Towards On-Device Face Recognition in Body-worn Cameras—0
Towards Optimal Compression: Joint Pruning and Quantization—0
Towards Practical and Efficient Image-to-Speech Captioning with Vision-Language Pre-training and Multi-modal Tokens—0
Towards Practical Single-shot Phase Retrieval with Physics-Driven Deep Neural Network—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