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 2976–3000 of 4925 papers

TitleStatusHype
A Fast Network Exploration Strategy to Profile Low Energy Consumption for Keyword Spotting—0
The Ecological Footprint of Neural Machine Translation SystemsCode0
Robust Vector Quantized-Variational Autoencoder—0
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
Few-Bit Backward: Quantized Gradients of Activation Functions for Memory Footprint ReductionCode1
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
The fine line between dead neurons and sparsity in binarized spiking neural networksCode1
Neural JPEG: End-to-End Image Compression Leveraging a Standard JPEG Encoder-Decoder—0
Neural Network based Inter bi-prediction Blending—0
Post-training Quantization for Neural Networks with Provable GuaranteesCode1
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
FAT: An In-Memory Accelerator with Fast Addition for Ternary Weight Neural Networks—0
Q-ViT: Fully Differentiable Quantization for Vision TransformerCode1
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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