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 4351–4400 of 4925 papers

TitleStatusHype
Efficient and accurate neural field reconstruction using resistive memory—0
Efficient and Effective Methods for Mixed Precision Neural Network Quantization for Faster, Energy-efficient Inference—0
Efficient and Reliable Vector Similarity Search Using Asymmetric Encoding with NAND-Flash for Many-Class Few-Shot Learning—0
Efficient and Workload-Aware LLM Serving via Runtime Layer Swapping and KV Cache Resizing—0
Efficient ANN-SNN Conversion with Error Compensation Learning—0
Efficient Arbitrary Precision Acceleration for Large Language Models on GPU Tensor Cores—0
Efficient Asynchronous Federated Learning with Sparsification and Quantization—0
Efficient Batch Homomorphic Encryption for Vertically Federated XGBoost—0
Efficient Bitwidth Search for Practical Mixed Precision Neural Network—0
Efficient Channel Estimator with Angle-Division Multiple Access—0
Efficient Compression of Multitask Multilingual Speech Models—0
Efficient Convolutional Neural Network with Binary Quantization Layer—0
Efficient Decoupled Feature 3D Gaussian Splatting via Hierarchical Compression—0
Efficient Discrete Supervised Hashing for Large-scale Cross-modal Retrieval—0
Peering Beyond the Gradient Veil with Distributed Auto Differentiation—0
Efficient Distributed Training through Gradient Compression with Sparsification and Quantization Techniques—0
Egeria: Efficient DNN Training with Knowledge-Guided Layer Freezing—0
Efficient Error-Tolerant Quantized Neural Network Accelerators—0
Efficient Evaluation of Quantization-Effects in Neural Codecs—0
Efficient Execution of Quantized Deep Learning Models: A Compiler Approach—0
Efficient Federated Learning over Multiple Access Channel with Differential Privacy Constraints—0
Efficient Fine-Tuning of Quantized Models via Adaptive Rank and Bitwidth—0
Efficient FPGA-accelerated Convolutional Neural Networks for Cloud Detection on CubeSats—0
Efficient Generative Modeling with Residual Vector Quantization-Based Tokens—0
Efficient Hardware Implementation of Incremental Learning and Inference on Chip—0
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