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 1151–1175 of 4925 papers

TitleStatusHype
GLARE: Low Light Image Enhancement via Generative Latent Feature based Codebook RetrievalCode2
StoX-Net: Stochastic Processing of Partial Sums for Efficient In-Memory Computing DNN AcceleratorsCode0
FETCH: A Memory-Efficient Replay Approach for Continual Learning in Image Classification—0
Mamba-PTQ: Outlier Channels in Recurrent Large Language Models—0
Co-Designing Binarized Transformer and Hardware Accelerator for Efficient End-to-End Edge Deployment—0
Turbo: Informativity-Driven Acceleration Plug-In for Vision-Language Large ModelsCode1
Exploring Quantization for Efficient Pre-Training of Transformer Language ModelsCode1
Rate-Distortion-Cognition Controllable Versatile Neural Image Compression—0
Tiled Bit Networks: Sub-Bit Neural Network Compression Through Reuse of Learnable Binary Vectors—0
LRQ: Optimizing Post-Training Quantization for Large Language Models by Learning Low-Rank Weight-Scaling MatricesCode0
NITRO-D: Native Integer-only Training of Deep Convolutional Neural NetworksCode0
QVD: Post-training Quantization for Video Diffusion Models—0
Quality Scalable Quantization Methodology for Deep Learning on Edge—0
Fast Matrix Multiplications for Lookup Table-Quantized LLMsCode3
SEMINAR: Search Enhanced Multi-modal Interest Network and Approximate Retrieval for Lifelong Sequential Recommendation—0
Qwen2 Technical ReportCode13
Quantized Prompt for Efficient Generalization of Vision-Language ModelsCode0
LeanQuant: Accurate Large Language Model Quantization with Loss-Error-Aware Grid—0
A Bag of Tricks for Scaling CPU-based Deep FFMs to more than 300m Predictions per Second—0
One-Bit MIMO Detection: From Global Maximum-Likelihood Detector to Amplitude Retrieval Approach—0
Semi-supervised 3D Object Detection with PatchTeacher and PillarMixCode0
PSC: Posterior Sampling-Based CompressionCode1
Accuracy is Not All You Need—0
On Exact Bit-level Reversible Transformers Without Changing ArchitecturesCode1
Optimization of DNN-based speaker verification model through efficient quantization technique—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