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 1301–1350 of 4925 papers

TitleStatusHype
Characterising Bias in Compressed Models—0
2-bit Conformer quantization for automatic speech recognition—0
Channel-Wise Mixed-Precision Quantization for Large Language Models—0
APCodec+: A Spectrum-Coding-Based High-Fidelity and High-Compression-Rate Neural Audio Codec with Staged Training Paradigm—0
Adaptive Proximal Gradient Methods for Structured Neural Networks—0
Channel-wise Hessian Aware trace-Weighted Quantization of Neural Networks—0
Channel Pruning In Quantization-aware Training: An Adaptive Projection-gradient Descent-shrinkage-splitting Method—0
Channel Estimation in MIMO Systems with One-bit Spatial Sigma-delta ADCs—0
APack: Off-Chip, Lossless Data Compression for Efficient Deep Learning Inference—0
Efficient Quantum Approximate kNN Algorithm via Granular-Ball Computing—0
Efficient Vision-based Vehicle Speed Estimation—0
ELMGS: Enhancing memory and computation scaLability through coMpression for 3D Gaussian Splatting—0
End-to-end fully-binarized network design: from Generic Learned Thermometer to Block Pruning—0
Enhancing Bridge Deck Delamination Detection Based on Aerial Thermography Through Grayscale Morphologic Reconstruction: A Case Study—0
Channel Estimation for MIMO Hybrid Architectures with Low Resolution ADCs for mmWave Communication—0
Channel Balancing for Accurate Quantization of Winograd Convolutions—0
Channel-Aware Constellation Design for Digital OTA Computation—0
Adaptive Precision Training: Quantify Back Propagation in Neural Networks with Fixed-point Numbers—0
HAWKEYE: Adversarial Example Detector for Deep Neural Networks—0
Challenging GPU Dominance: When CPUs Outperform for On-Device LLM Inference—0
Order of Compression: A Systematic and Optimal Sequence to Combinationally Compress CNN—0
An Ultra-Efficient Memristor-Based DNN Framework with Structured Weight Pruning and Quantization Using ADMM—0
ANTLER: Bayesian Nonlinear Tensor Learning and Modeler for Unstructured, Varying-Size Point Cloud Data—0
Cell growth rate dictates the onset of glass to fluid-like transition and long time super-diffusion in an evolving cell colony—0
Adaptive Periodic Averaging: A Practical Approach to Reducing Communication in Distributed Learning—0
Efficient Neural PDE-Solvers using Quantization Aware Training—0
Efficient On-the-fly Category Retrieval using ConvNets and GPUs—0
CEGI: Measuring the trade-off between efficiency and carbon emissions for SLMs and VLMs—0
CEG4N: Counter-Example Guided Neural Network Quantization Refinement—0
CDQuant: Greedy Coordinate Descent for Accurate LLM Quantization—0
CDC: Classification Driven Compression for Bandwidth Efficient Edge-Cloud Collaborative Deep Learning—0
An Overview on IEEE 802.11bf: WLAN Sensing—0
CBQ: Cross-Block Quantization for Large Language Models—0
Causal Speech Enhancement with Predicting Semantics based on Quantized Self-supervised Learning Features—0
An Overview of Neural Network Compression—0
Adaptive Low-Precision Training for Embeddings in Click-Through Rate Prediction—0
An Overview of Datatype Quantization Techniques for Convolutional Neural Networks—0
Starting Positions Matter: A Study on Better Weight Initialization for Neural Network Quantization—0
Efficient Neural Compression with Inference-time Decoding—0
Efficient Neural Networks for Tiny Machine Learning: A Comprehensive Review—0
Efficient Point Transformer for Large-scale 3D Scene Understanding—0
Efficiently Scaling Transformer Inference—0
Discrete Audio Tokens: More Than a Survey!—0
Efficient Machine Translation with Model Pruning and Quantization—0
A Novel Unified Model for Multi-exposure Stereo Coding Based on Low Rank Tucker-ALS and 3D-HEVC—0
STBLLM: Breaking the 1-Bit Barrier with Structured Binary LLMs—0
Efficient Match Kernel between Sets of Features for Visual Recognition—0
Can Large Language Models Understand Context?—0
A Novel Structure-Agnostic Multi-Objective Approach for Weight-Sharing Compression in Deep Neural Networks—0
Can General-Purpose Large Language Models Generalize to English-Thai Machine Translation ?—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