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 1226–1250 of 4925 papers

TitleStatusHype
A Practical Mixed Precision Algorithm for Post-Training Quantization—0
Codebook based Audio Feature Representation for Music Information Retrieval—0
Codage \'echelonnable \`a granularit\'e fine de la parole : Application au codeur G.729 (Fine granularity scalable speech coding: Application to the G.729 coder) [in French]—0
Adaptive Wireless Image Semantic Transmission and Over-The-Air Testing—0
Cocktail: Chunk-Adaptive Mixed-Precision Quantization for Long-Context LLM Inference—0
Approximation speed of quantized vs. unquantized ReLU neural networks and beyond—0
CoAst: Validation-Free Contribution Assessment for Federated Learning based on Cross-Round Valuation—0
Approximation of functions with one-bit neural networks—0
Adaptive Transmission for Distributed Detection in Energy Harvesting Wireless Sensor Networks—0
2-in-1 Accelerator: Enabling Random Precision Switch for Winning Both Adversarial Robustness and Efficiency—0
E^2BoWs: An End-to-End Bag-of-Words Model via Deep Convolutional Neural Network—0
COAP: Memory-Efficient Training with Correlation-Aware Gradient Projection—0
CNN inference acceleration using dictionary of centroids—0
Accelerating Energy-Efficient Federated Learning in Cell-Free Networks with Adaptive Quantization—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
Reconstruction of Privacy-Sensitive Data from Protected Templates—0
CNN-based Analog CSI Feedback in FDD MIMO-OFDM Systems—0
CNN Acceleration by Low-rank Approximation with Quantized Factors—0
Approximate search with quantized sparse representations—0
CNN2Gate: Toward Designing a General Framework for Implementation of Convolutional Neural Networks on FPGA—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
DynaQuant: Compressing Deep Learning Training Checkpoints via Dynamic Quantization—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