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 2176–2200 of 4925 papers

TitleStatusHype
Intelligent Fault Diagnosis of Type and Severity in Low-Frequency, Low Bit-Depth Signals—0
Interactions Across Blocks in Post-Training Quantization of Large Language Models—0
Benchmarking the Robustness of Quantized Models—0
Differentiable Search for Finding Optimal Quantization Strategy—0
Integer or Floating Point? New Outlooks for Low-Bit Quantization on Large Language Models—0
Benchmarking the Reliability of Post-training Quantization: a Particular Focus on Worst-case Performance—0
Analog-digital Scheduling for Federated Learning: A Communication-Efficient Approach—0
Integer Scale: A Free Lunch for Faster Fine-grained Quantization of LLMs—0
Differentiable Product Quantization for Learning Compact Embedding Layers—0
An Additive Latent Feature Model for Transparent Object Recognition—0
Benchmarking quantized LLaMa-based models on the Brazilian Secondary School Exam—0
ACQ: Improving Generative Data-free Quantization Via Attention Correction—0
Differentiable Joint Pruning and Quantization for Hardware Efficiency—0
An Adaptive Statistical Non-uniform Quantizer for Detail Wavelet Components in Lossy JPEG2000 Image Compression—0
Acoustic Model Compression with MAP adaptation—0
Differentiable Dynamic Quantization with Mixed Precision and Adaptive Resolution—0
Differentiable Discrete Device-to-System Codesign for Optical Neural Networks via Gumbel-Softmax—0
Benchmarking CFAR and CNN-based Peak Detection Algorithms in ISAC under Hardware Impairments—0
A Bag of Tricks for Scaling CPU-based Deep FFMs to more than 300m Predictions per Second—0
Diagnostic data integration using deep neural networks for real-time plasma analysis—0
BELT:Bootstrapping Electroencephalography-to-Language Decoding and Zero-Shot Sentiment Classification by Natural Language Supervision—0
DFTerNet: Towards 2-bit Dynamic Fusion Networks for Accurate Human Activity Recognition—0
An adaptive random experiment design method for engineering experiment—0
2-bit Model Compression of Deep Convolutional Neural Network on ASIC Engine for Image Retrieval—0
Integrating PHY Security Into NDN-IoT Networks By Exploiting MEC: Authentication Efficiency, Robustness, and Accuracy Enhancement—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