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 2226–2250 of 4925 papers

TitleStatusHype
BBQRec: Behavior-Bind Quantization for Multi-Modal Sequential Recommendation—0
A Modular Neural Network Based Deep Learning Approach for MIMO Signal Detection—0
Designing strong baselines for ternary neural network quantization through support and mass equalization—0
Designing DNNs for a trade-off between robustness and processing performance in embedded devices—0
b-bit Marginal Regression—0
Designing Discontinuities—0
Designing a Classifier for Active Fire Detection from Multispectral Satellite Imagery Using Neural Architecture Search—0
Bayes Merging of Multiple Vocabularies for Scalable Image Retrieval—0
A Compressed Sensing Approach for Distribution Matching—0
A 65nm 8b-Activation 8b-Weight SRAM-Based Charge-Domain Computing-in-Memory Macro Using A Fully-Parallel Analog Adder Network and A Single-ADC Interface—0
Design Flow of Accelerating Hybrid Extremely Low Bit-width Neural Network in Embedded FPGA—0
Design Automation for Efficient Deep Learning Computing—0
Bayesian-LoRA: LoRA based Parameter Efficient Fine-Tuning using Optimal Quantization levels and Rank Values trough Differentiable Bayesian Gates—0
Design and Analysis of Uplink and Downlink Communications for Federated Learning—0
Design and Analysis of Hardware-limited Non-uniform Task-based Quantizers—0
A method of using RSVD in residual calculation of LowBit GEMM—0
DeRS: Towards Extremely Efficient Upcycled Mixture-of-Experts Models—0
Derived Codebooks for High-Accuracy Nearest Neighbor Search—0
A Comprehensive Survey on Vector Database: Storage and Retrieval Technique, Challenge—0
Dequantization of a signal from two parallel quantized observations—0
Breaking the Hourglass Phenomenon of Residual Quantization: Enhancing the Upper Bound of Generative Retrieval—0
Iteratively Training Look-Up Tables for Network Quantization—0
Iteratively Training Look-Up Tables for Network Quantization—0
Iterative Signal Processing for Integrated Sensing and Communication Systems—0
JND-Based Perceptual Optimization For Learned Image Compression—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