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 1076–1100 of 4925 papers

TitleStatusHype
Jamba-1.5: Hybrid Transformer-Mamba Models at ScaleCode5
Matmul or No Matmal in the Era of 1-bit LLMs—0
MARLIN: Mixed-Precision Auto-Regressive Parallel Inference on Large Language ModelsCode5
Disentangling segmental and prosodic factors to non-native speech comprehensibility—0
Hyperstroke: A Novel High-quality Stroke Representation for Assistive Artistic Drawing—0
Explore Cross-Codec Quality-Rate Convex Hulls Relation for Adaptive Streaming—0
ABQ-LLM: Arbitrary-Bit Quantized Inference Acceleration for Large Language ModelsCode3
Efficient Autoregressive Audio Modeling via Next-Scale PredictionCode2
JPEG-LM: LLMs as Image Generators with Canonical Codec Representations—0
PQV-Mobile: A Combined Pruning and Quantization Toolkit to Optimize Vision Transformers for Mobile ApplicationsCode0
Analog Spiking Neuron in CMOS 28 nm Towards Large-Scale Neuromorphic Processors—0
Line Spectral Estimation with Unlimited Sensing—0
Prompt Tuning as User Inherent Profile Inference Machine—0
Low-Bitwidth Floating Point Quantization for Efficient High-Quality Diffusion Models—0
Computability of Classification and Deep Learning: From Theoretical Limits to Practical Feasibility through Quantization—0
RTF-Q: Efficient Unsupervised Domain Adaptation with Retraining-free Quantization—0
SWIFT:A Scalable lightWeight Infrastructure for Fine-TuningCode11
Quantum-secure multiparty deep learning—0
Semantic-Enabled 6G Communication: A Task-oriented and Privacy-preserving Perspective—0
FDC: Fast KV Dimensionality Compression for Efficient LLM Inference—0
Advancing Multimodal Large Language Models with Quantization-Aware Scale Learning for Efficient AdaptationCode1
Compact 3D Gaussian Splatting for Static and Dynamic Radiance FieldsCode3
Inference Optimizations for Large Language Models: Effects, Challenges, and Practical Considerations—0
L3iTC at the FinLLM Challenge Task: Quantization for Financial Text Classification & Summarization—0
EC-Guide: A Comprehensive E-Commerce Guide for Instruction Tuning and QuantizationCode1
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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