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 1301–1325 of 4925 papers

TitleStatusHype
AIQViT: Architecture-Informed Post-Training Quantization for Vision Transformers—0
A Performance Analysis of You Only Look Once Models for Deployment on Constrained Computational Edge Devices in Drone Applications—0
KVTuner: Sensitivity-Aware Layer-wise Mixed Precision KV Cache Quantization for Efficient and Nearly Lossless LLM InferenceCode0
TQ-DiT: Efficient Time-Aware Quantization for Diffusion Transformers—0
Exploring Model Invariance with Discrete Search for Ultra-Low-Bit Quantization—0
Asymptotic Analysis of One-bit Quantized Box-Constrained Precoding in Large-Scale Multi-User Systems—0
HACK: Homomorphic Acceleration via Compression of the Key-Value Cache for Disaggregated LLM Inference—0
SensorChat: Answering Qualitative and Quantitative Questions during Long-Term Multimodal Sensor Interactions—0
BRIDLE: Generalized Self-supervised Learning with QuantizationCode0
Survey of Quantization Techniques for On-Device Vision-based Crack Detection—0
Unlocking Efficient Large Inference Models: One-Bit Unrolling Tips the Scales—0
Continuous Autoregressive Modeling with Stochastic Monotonic Alignment for Speech Synthesis—0
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
An Inquiry into Datacenter TCO for LLM Inference with FP8—0
Nearly Lossless Adaptive Bit SwitchingCode0
Huff-LLM: End-to-End Lossless Compression for Efficient LLM Inference—0
On Noncommutative Quantum Mechanics and the Black-Scholes Model—0
Structural Latency Perturbation in Large Language Models Through Recursive State Induction—0
Enhancing Field-Oriented Control of Electric Drives with Tiny Neural Network Optimized for Micro-controllers—0
MQuant: Unleashing the Inference Potential of Multimodal Large Language Models via Full Static Quantization—0
LLM-based Affective Text Generation Quality Based on Different Quantization Values—0
Fully Distributed and Quantized Algorithm for MPC-based Autonomous Vehicle Platooning Optimization—0
CodeBrain: Impute Any Brain MRI via Instance-specific Scalar-quantized Codes—0
Mixed-Precision Graph Neural Quantization for Low Bit Large Language Models—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