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 3201–3225 of 4925 papers

TitleStatusHype
TinyM^2Net: A Flexible System Algorithm Co-designed Multimodal Learning Framework for Tiny Devices—0
Lightweight Jet Reconstruction and Identification as an Object Detection Task—0
Binary Neural Networks as a general-propose compute paradigm for on-device computer vision—0
Robust Semantic Communications Against Semantic Noise—0
Energy awareness in low precision neural networks—0
A Fast Network Exploration Strategy to Profile Low Energy Consumption for Keyword Spotting—0
Robust Vector Quantized-Variational Autoencoder—0
The Ecological Footprint of Neural Machine Translation SystemsCode0
PRUNIX: Non-Ideality Aware Convolutional Neural Network Pruning for Memristive Accelerators—0
Adaptive Discrete Communication Bottlenecks with Dynamic Vector Quantization—0
Does Video Compression Impact Tracking Accuracy?—0
Leveraging Bitstream Metadata for Fast, Accurate, Generalized Compressed Video Quality Enhancement—0
Neural-PIM: Efficient Processing-In-Memory with Neural Approximation of Peripherals—0
Training Thinner and Deeper Neural Networks: Jumpstart RegularizationCode0
Deep Task-Based Analog-to-Digital ConversionCode0
Bioinspired Cortex-based Fast Codebook Generation—0
Neural JPEG: End-to-End Image Compression Leveraging a Standard JPEG Encoder-Decoder—0
Neural Network based Inter bi-prediction Blending—0
Resource-efficient Deep Neural Networks for Automotive Radar Interference Mitigation—0
Spectral-PQ: A Novel Spectral Sensitivity-Orientated Perceptual Compression Technique for RGB 4:4:4 Video Data—0
DCNGAN: A Deformable Convolutional-Based GAN with QP Adaptation for Perceptual Quality Enhancement of Compressed Video—0
APack: Off-Chip, Lossless Data Compression for Efficient Deep Learning Inference—0
Neural Network Quantization with AI Model Efficiency Toolkit (AIMET)—0
What can we learn from misclassified ImageNet images?—0
HEAM: High-Efficiency Approximate Multiplier Optimization for Deep Neural NetworksCode0
Show:102550
← PrevPage 129 of 197Next →
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