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

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Showing 1476–1500 of 4925 papers

TitleStatusHype
Exploring Quantization and Mapping Synergy in Hardware-Aware Deep Neural Network AcceleratorsCode0
Have You Merged My Model? On The Robustness of Large Language Model IP Protection Methods Against Model MergingCode1
BinaryDM: Accurate Weight Binarization for Efficient Diffusion ModelsCode1
Physics of Language Models: Part 3.3, Knowledge Capacity Scaling Laws—0
Investigating the Impact of Quantization on Adversarial Robustness—0
David and Goliath: An Empirical Evaluation of Attacks and Defenses for QNNs at the Deep EdgeCode0
Nanometer Scanning with Micrometer Sensing: Beating Quantization Constraints in Lissajous Trajectory Tracking—0
Gull: A Generative Multifunctional Audio Codec—0
Weakly Supervised Deep Hyperspherical Quantization for Image RetrievalCode0
What Happens When Small Is Made Smaller? Exploring the Impact of Compression on Small Data Pretrained Language Models—0
Fine-Tuning, Quantization, and LLMs: Navigating Unintended Outcomes—0
Outlier-Efficient Hopfield Layers for Large Transformer-Based ModelsCode1
Mitigating the Impact of Outlier Channels for Language Model Quantization with Activation RegularizationCode0
TinyVQA: Compact Multimodal Deep Neural Network for Visual Question Answering on Resource-Constrained Devices—0
AdaBM: On-the-Fly Adaptive Bit Mapping for Image Super-ResolutionCode2
DI-Retinex: Digital-Imaging Retinex Theory for Low-Light Image Enhancement—0
CLaM-TTS: Improving Neural Codec Language Model for Zero-Shot Text-to-Speech—0
Cherry on Top: Parameter Heterogeneity and Quantization in Large Language Models—0
Efficient Multi-Vector Dense Retrieval Using Bit VectorsCode2
DNN Memory Footprint Reduction via Post-Training Intra-Layer Multi-Precision Quantization—0
PiSSA: Principal Singular Values and Singular Vectors Adaptation of Large Language ModelsCode3
NeRFCodec: Neural Feature Compression Meets Neural Radiance Fields for Memory-Efficient Scene Representation—0
On the Effect of Quantization on Dynamic Mode Decomposition—0
RefQSR: Reference-based Quantization for Image Super-Resolution Networks—0
Minimize Quantization Output Error with Bias CompensationCode0
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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