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 1601–1625 of 4925 papers

TitleStatusHype
A Comprehensive Evaluation of Quantization Strategies for Large Language ModelsCode0
Data-freeWeight Compress and Denoise for Large Language Models—0
LLM Inference Unveiled: Survey and Roofline Model InsightsCode4
Self-Supervised Speech Quality Estimation and Enhancement Using Only Clean SpeechCode2
EncodingNet: A Novel Encoding-based MAC Design for Efficient Neural Network AccelerationCode0
Towards Accurate Post-training Quantization for Reparameterized ModelsCode0
GPTVQ: The Blessing of Dimensionality for LLM Quantization—0
On the Arrow of Inference—0
Text me the data: Generating Ground Pressure Sequence from Textual Descriptions for HAR—0
APTQ: Attention-aware Post-Training Mixed-Precision Quantization for Large Language Models—0
Distillation Contrastive Decoding: Improving LLMs Reasoning with Contrastive Decoding and DistillationCode1
FinGPT-HPC: Efficient Pretraining and Finetuning Large Language Models for Financial Applications with High-Performance Computing—0
In-Distribution Consistency Regularization Improves the Generalization of Quantization-Aware Training—0
Understanding and Mitigating the Threat of Vec2Text to Dense Retrieval SystemsCode1
Tiny Reinforcement Learning for Quadruped Locomotion using Decision TransformersCode0
Language-Codec: Bridging Discrete Codec Representations and Speech Language ModelsCode3
Towards a tailored mixed-precision sub-8-bit quantization scheme for Gated Recurrent Units using Genetic Algorithms—0
WKVQuant: Quantizing Weight and Key/Value Cache for Large Language Models Gains More—0
Is It a Free Lunch for Removing Outliers during Pretraining?—0
DB-LLM: Accurate Dual-Binarization for Efficient LLMs—0
LaCo: Large Language Model Pruning via Layer CollapseCode1
OneBit: Towards Extremely Low-bit Large Language ModelsCode3
Hierarchical Prior-based Super Resolution for Point Cloud Geometry CompressionCode1
Any-Precision LLM: Low-Cost Deployment of Multiple, Different-Sized LLMsCode2
EdgeQAT: Entropy and Distribution Guided Quantization-Aware Training for the Acceleration of Lightweight LLMs on the EdgeCode1
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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