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 4476–4500 of 4925 papers

TitleStatusHype
Error Compensated Quantized SGD and its Applications to Large-scale Distributed Optimization—0
Error Feedback Approach for Quantization Noise Reduction of Distributed Graph Filters—0
ERVQ: Enhanced Residual Vector Quantization with Intra-and-Inter-Codebook Optimization for Neural Audio Codecs—0
eSampling: Energy Harvesting ADCs—0
ESC-MVQ: End-to-End Semantic Communication With Multi-Codebook Vector Quantization—0
ESE: Efficient Speech Recognition Engine with Sparse LSTM on FPGA—0
Estimating the Completeness of Discrete Speech Units—0
Estimation and Quantization of Expected Persistence Diagrams—0
EuclidNets: An Alternative Operation for Efficient Inference of Deep Learning Models—0
EuclidNets: Combining hardware and architecture design for Efficient Inference and Training—0
Evaluating Post-Training Compression in GANs using Locality-Sensitive Hashing—0
Evaluating the Practicality of Learned Image Compression—0
Evaluation of Linear Implicit Quantized State System method for analyzing mission performance of power systems—0
Evaluation of quality measures for color quantization—0
Event-Based Bispectral Photometry Using Temporally Modulated Illumination—0
Eventor: An Efficient Event-Based Monocular Multi-View Stereo Accelerator on FPGA Platform—0
Event Retrieval in Large Video Collections with Circulant Temporal Encoding—0
Distributed Inference with Sparse and Quantized Communication—0
Event-Triggered Quantized Average Consensus via Mass Summation—0
Exact Bias Correction and Covariance Estimation for Stereo Vision—0
Exact Recovery of Sparse Binary Vectors from Generalized Linear Measurements—0
Examining the Role and Limits of Batchnorm Optimization to Mitigate Diverse Hardware-noise in In-memory Computing—0
eXmY: A Data Type and Technique for Arbitrary Bit Precision Quantization—0
Expand-and-Quantize: Unsupervised Semantic Segmentation Using High-Dimensional Space and Product Quantization—0
Expectation maximization transfer learning and its application for bionic hand prostheses—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