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 3201–3250 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
FAT: An In-Memory Accelerator with Fast Addition for Ternary Weight Neural Networks—0
Data-Driven Deep Learning Based Hybrid Beamforming for Aerial Massive MIMO-OFDM Systems with Implicit CSI—0
Design Space Exploration of Dense and Sparse Mapping Schemes for RRAM Architectures—0
Tk-merge: Computationally Efficient Robust Clustering Under General Assumptions—0
Improving Performance of Semantic Segmentation CycleGANs by Noise Injection into the Latent Segmentation Space—0
VAQF: Fully Automatic Software-Hardware Co-Design Framework for Low-Bit Vision Transformer—0
Neural Network Compression of ACAS Xu Early Prototype is Unsafe: Closed-Loop Verification through Quantized State BackreachabilityCode0
UWC: Unit-wise Calibration Towards Rapid Network Compression—0
Egeria: Efficient DNN Training with Knowledge-Guided Layer Freezing—0
Boosted Dense Retriever—0
UDC: Unified DNAS for Compressible TinyML Models—0
ViT2Hash: Unsupervised Information-Preserving Hashing—0
A Physics-Informed Vector Quantized Autoencoder for Data Compression of Turbulent FlowCode0
A Planck Radiation and Quantization Scheme for Human Cognition and Language—0
Bandlimited signal reconstruction from leaky integrate-and-fire encoding using POCS—0
An Adaptive Device-Edge Co-Inference Framework Based on Soft Actor-Critic—0
High-contrast, speckle-free, true 3D holography via binary CGH optimization—0
Optimizing the Communication-Accuracy Trade-off in Federated Learning with Rate-Distortion TheoryCode0
Video Coding for Machines: Partial transmission of SIFT features—0
Detection of extragalactic Ultra-Compact Dwarfs and Globular Clusters using Explainable AI techniquesCode0
Elastic Product Quantization for Time SeriesCode0
A Robust Visual Sampling Model Inspired by Receptive Field—0
IQDUBBING: Prosody modeling based on discrete self-supervised speech representation for expressive voice conversion—0
RecDis-SNN: Rectifying Membrane Potential Distribution for Directly Training Spiking Neural Networks—0
Channel Balancing for Accurate Quantization of Winograd Convolutions—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