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 2101–2125 of 4925 papers

TitleStatusHype
MLP Fusion: Towards Efficient Fine-tuning of Dense and Mixture-of-Experts Language ModelsCode1
Grounded Object Centric Learning—0
Low bit rate binaural link for improved ultra low-latency low-complexity multichannel speech enhancement in Hearing Aids—0
Extreme Image Compression using Fine-tuned VQGANs—0
A Survey of Techniques for Optimizing Transformer Inference—0
Do Emergent Abilities Exist in Quantized Large Language Models: An Empirical StudyCode1
Learning Kernel-Modulated Neural Representation for Efficient Light Field Compression—0
Self-Distilled Quantization: Achieving High Compression Rates in Transformer-Based Language Models—0
Mixed-Precision Quantization for Deep Vision Models with Integer Quadratic ProgrammingCode0
Minimax Excess Risk of First-Order Methods for Statistical Learning with Data-Dependent Oracles—0
Q-YOLOP: Quantization-aware You Only Look Once for Panoptic Driving Perception—0
QBitOpt: Fast and Accurate Bitwidth Reallocation during Training—0
InfLoR-SNN: Reducing Information Loss for Spiking Neural Networks—0
Towards Efficient In-memory Computing Hardware for Quantized Neural Networks: State-of-the-art, Open Challenges and Perspectives—0
KP2Dtiny: Quantized Neural Keypoint Detection and Description on the EdgeCode0
ITA: An Energy-Efficient Attention and Softmax Accelerator for Quantized Transformers—0
INT-FP-QSim: Mixed Precision and Formats For Large Language Models and Vision TransformersCode1
Free Bits: Latency Optimization of Mixed-Precision Quantized Neural Networks on the Edge—0
Pruning vs Quantization: Which is Better?Code1
Dequantization and Color Transfer with Diffusion ModelsCode0
Fast Private Kernel Density Estimation via Locality Sensitive QuantizationCode0
Greedy Selection for Heterogeneous Sensors—0
Line Spectrum Estimation and Detection with Few-bit ADCs: Theoretical Analysis and Generalized NOMP Algorithm—0
Data-Free Quantization via Mixed-Precision Compensation without Fine-Tuning—0
Q-YOLO: Efficient Inference for Real-time Object Detection—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