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 976–1000 of 4925 papers

TitleStatusHype
LegalEval-Q: A New Benchmark for The Quality Evaluation of LLM-Generated Legal TextCode0
Edge Computing for Physics-Driven AI in Computational MRI: A Feasibility Study—0
Running Conventional Automatic Speech Recognition on Memristor Hardware: A Simulated Approach—0
LittleBit: Ultra Low-Bit Quantization via Latent Factorization—0
MuLoCo: Muon is a practical inner optimizer for DiLoCo—0
Efficient Quantum Approximate kNN Algorithm via Granular-Ball Computing—0
Merge-Friendly Post-Training Quantization for Multi-Target Domain AdaptationCode0
Revisiting Uncertainty Estimation and Calibration of Large Language Models—0
Highly Efficient and Effective LLMs with Multi-Boolean Architectures—0
Climate Finance BenchCode0
On the Interplay of Privacy, Persuasion and Quantization—0
Does quantization affect models' performance on long-context tasks?Code0
Small Language Models: Architectures, Techniques, Evaluation, Problems and Future Adaptation—0
LPCM: Learning-based Predictive Coding for LiDAR Point Cloud Compression—0
CA3D: Convolutional-Attentional 3D Nets for Efficient Video Activity Recognition on the Edge—0
BrainStratify: Coarse-to-Fine Disentanglement of Intracranial Neural Dynamics—0
Optimizing edge AI models on HPC systems with the edge in the loopCode0
Efficient Speech Translation through Model Compression and Knowledge DistillationCode0
Communication-Efficient Multi-Device Inference Acceleration for Transformer ModelsCode0
FastMamba: A High-Speed and Efficient Mamba Accelerator on FPGA with Accurate Quantization—0
LoTA-QAF: Lossless Ternary Adaptation for Quantization-Aware Fine-TuningCode0
Adaptive Prediction-Powered AutoEval with Reliability and Efficiency GuaranteesCode0
Distinctive Feature Codec: Adaptive Segmentation for Efficient Speech Representation—0
Efficient and Workload-Aware LLM Serving via Runtime Layer Swapping and KV Cache Resizing—0
Reducing Storage of Pretrained Neural Networks by Rate-Constrained Quantization and Entropy CodingCode0
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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