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 2151–2175 of 4925 papers

TitleStatusHype
Adaptive Transmission for Distributed Detection in Energy Harvesting Wireless Sensor Networks—0
Accelerating Inference and Language Model Fusion of Recurrent Neural Network Transducers via End-to-End 4-bit Quantization—0
Evaluating the Practicality of Learned Image Compression—0
COAP: Memory-Efficient Training with Correlation-Aware Gradient Projection—0
CNN inference acceleration using dictionary of centroids—0
Evaluating Post-Training Compression in GANs using Locality-Sensitive Hashing—0
CNN-Based Equalization for Communications: Achieving Gigabit Throughput with a Flexible FPGA Hardware Architecture—0
Adaptive Training of Random Mapping for Data Quantization—0
EuclidNets: Combining hardware and architecture design for Efficient Inference and Training—0
EuclidNets: An Alternative Operation for Efficient Inference of Deep Learning Models—0
CNN-based Analog CSI Feedback in FDD MIMO-OFDM Systems—0
Estimation and Quantization of Expected Persistence Diagrams—0
CNN Acceleration by Low-rank Approximation with Quantized Factors—0
Approximate search with quantized sparse representations—0
Estimating the Completeness of Discrete Speech Units—0
CNN2Gate: Toward Designing a General Framework for Implementation of Convolutional Neural Networks on FPGA—0
ESE: Efficient Speech Recognition Engine with Sparse LSTM on FPGA—0
ESC-MVQ: End-to-End Semantic Communication With Multi-Codebook Vector Quantization—0
Cluster Regularized Quantization for Deep Networks Compression—0
Approximate Probabilistic Neural Networks with Gated Threshold Logic—0
Adaptive Sample-space & Adaptive Probability coding: a neural-network based approach for compression—0
eSampling: Energy Harvesting ADCs—0
ERVQ: Enhanced Residual Vector Quantization with Intra-and-Inter-Codebook Optimization for Neural Audio Codecs—0
Towards Feature Distribution Alignment and Diversity Enhancement for Data-Free Quantization—0
Error Feedback Approach for Quantization Noise Reduction of Distributed Graph Filters—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