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 501–525 of 4925 papers

TitleStatusHype
Survey of Quantization Techniques for On-Device Vision-based Crack Detection—0
Unlocking Efficient Large Inference Models: One-Bit Unrolling Tips the Scales—0
Massive Values in Self-Attention Modules are the Key to Contextual Knowledge UnderstandingCode2
QLESS: A Quantized Approach for Data Valuation and Selection in Large Language Model Fine-TuningCode0
Choose Your Model Size: Any Compression by a Single Gradient Descent—0
Continuous Autoregressive Modeling with Stochastic Monotonic Alignment for Speech Synthesis—0
An Inquiry into Datacenter TCO for LLM Inference with FP8—0
Nearly Lossless Adaptive Bit SwitchingCode0
Structural Latency Perturbation in Large Language Models Through Recursive State Induction—0
Huff-LLM: End-to-End Lossless Compression for Efficient LLM Inference—0
On Noncommutative Quantum Mechanics and the Black-Scholes Model—0
MQuant: Unleashing the Inference Potential of Multimodal Large Language Models via Full Static Quantization—0
Enhancing Field-Oriented Control of Electric Drives with Tiny Neural Network Optimized for Micro-controllers—0
Cache Me If You Must: Adaptive Key-Value Quantization for Large Language ModelsCode1
Fully Distributed and Quantized Algorithm for MPC-based Autonomous Vehicle Platooning Optimization—0
Visual Autoregressive Modeling for Image Super-ResolutionCode2
LLM-based Affective Text Generation Quality Based on Different Quantization Values—0
Mixed-Precision Graph Neural Quantization for Low Bit Large Language Models—0
CodeBrain: Impute Any Brain MRI via Instance-specific Scalar-quantized Codes—0
Distinguished Quantized Guidance for Diffusion-based Sequence Recommendation—0
Post-Training Quantization for Vision Mamba with k-Scaled Quantization and Reparameterization—0
Post-Training Quantization for 3D Medical Image Segmentation: A Practical Study on Real Inference EnginesCode0
EdgeMLOps: Operationalizing ML models with Cumulocity IoT and thin-edge.io for Visual quality Inspection—0
Optimizing Large Language Model Training Using FP4 Quantization—0
One-Bit Sigma-Delta DFRC Waveform Design: Using Quantization Noise for Radar Probing—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