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 4376–4400 of 4925 papers

TitleStatusHype
Efficient Hybrid Network Architectures for Extremely Quantized Neural Networks Enabling Intelligence at the Edge—0
Efficient Implicit Neural Compression of Point Clouds via Learnable Activation in Latent Space—0
Efficient Inference via Universal LSH Kernel—0
Efficient Inferencing of Compressed Deep Neural Networks—0
Efficient Large-Scale Approximate Nearest Neighbor Search on OpenCL FPGA—0
Efficient Learned Lossless JPEG Recompression—0
EfficientLLM: Efficiency in Large Language Models—0
Efficiently Scaling Transformer Inference—0
Efficient Machine Translation with Model Pruning and Quantization—0
Efficient Match Kernel between Sets of Features for Visual Recognition—0
Efficient multivariate sequence classification—0
Efficient Neural Compression with Inference-time Decoding—0
Efficient Neural Networks for Tiny Machine Learning: A Comprehensive Review—0
Efficient Neural PDE-Solvers using Quantization Aware Training—0
Efficient On-the-fly Category Retrieval using ConvNets and GPUs—0
Efficient Point Transformer for Large-scale 3D Scene Understanding—0
Efficient Quantization Strategies for Latent Diffusion Models—0
Efficient Quantum Approximate kNN Algorithm via Granular-Ball Computing—0
Efficient Approximate Search for Sets of Vectors—0
Efficient Speech Representation Learning with Low-Bit Quantization—0
Efficient Storage of Fine-Tuned Models via Low-Rank Approximation of Weight Residuals—0
Efficient Super Resolution Using Binarized Neural Network—0
Efficient Systolic Array Based on Decomposable MAC for Quantized Deep Neural Networks—0
Efficient Vision-based Vehicle Speed Estimation—0
Efficient-VQGAN: Towards High-Resolution Image Generation with Efficient Vision Transformers—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