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 1451–1475 of 4925 papers

TitleStatusHype
EQ-Net: A Unified Deep Learning Framework for Log-Likelihood Ratio Estimation and Quantization—0
Efficient Discrete Supervised Hashing for Large-scale Cross-modal Retrieval—0
Error Analysis of CORDIC Processor with FPGA Implementation—0
Efficient Distributed Training through Gradient Compression with Sparsification and Quantization Techniques—0
Estimation and Quantization of Expected Persistence Diagrams—0
Event-Triggered Quantized Average Consensus via Mass Summation—0
BTEL: A Binary Tree Encoding Approach for Visual Localization—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
Bullion: A Column Store for Machine Learning—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
Edge Intelligence Optimization for Large Language Model Inference with Batching and Quantization—0
Efficient Hybrid Network Architectures for Extremely Quantized Neural Networks Enabling Intelligence at the Edge—0
Edge Inference with Fully Differentiable Quantized Mixed Precision Neural Networks—0
Efficient Inference via Universal LSH Kernel—0
Boost CTR Prediction for New Advertisements via Modeling Visual Content—0
EdgeFusion: On-Device Text-to-Image Generation—0
Edge-Enabled Real-time Railway Track Segmentation—0
Efficient Large-Scale Approximate Nearest Neighbor Search on OpenCL FPGA—0
BOMP-NAS: Bayesian Optimization Mixed Precision NAS—0
Show:102550
← PrevPage 59 of 197Next →
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