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 1476–1500 of 4925 papers

TitleStatusHype
EfficientLLM: Efficiency in Large Language Models—0
Edge Inference with Fully Differentiable Quantized Mixed Precision Neural Networks—0
Boost CTR Prediction for New Advertisements via Modeling Visual Content—0
EdgeFusion: On-Device Text-to-Image Generation—0
Efficient Match Kernel between Sets of Features for Visual Recognition—0
CALM: Co-evolution of Algorithms and Language Model for Automatic Heuristic Design—0
Efficient multivariate sequence classification—0
Edge-Enabled Real-time Railway Track Segmentation—0
Efficient Neural Compression with Inference-time Decoding—0
Efficient Neural Networks for Tiny Machine Learning: A Comprehensive Review—0
Efficient Neural PDE-Solvers using Quantization Aware Training—0
BOMP-NAS: Bayesian Optimization Mixed Precision NAS—0
Efficient On-the-fly Category Retrieval using ConvNets and GPUs—0
Efficient Point Transformer for Large-scale 3D Scene Understanding—0
An improved wavelet-based signal-denoising architecture with less hardware consumption—0
Edge-Efficient Deep Learning Models for Automatic Modulation Classification: A Performance Analysis—0
Can Large Language Models Understand Context?—0
Edge Deep Learning for Neural Implants—0
Edge Computing for Physics-Driven AI in Computational MRI: A Feasibility Study—0
An Improved BKW Algorithm for LWE with Applications to Cryptography and Lattices—0
Efficient Quantum Approximate kNN Algorithm via Granular-Ball Computing—0
Adaptive Block Floating-Point for Analog Deep Learning Hardware—0
EuclidNets: An Alternative Operation for Efficient Inference of Deep Learning Models—0
EdgeBERT: Sentence-Level Energy Optimizations for Latency-Aware Multi-Task NLP Inference—0
Edge AI: Evaluation of Model Compression Techniques for Convolutional Neural Networks—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