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 3001–3025 of 4925 papers

TitleStatusHype
Quantized Sparse Weight Decomposition for Neural Network Compression—0
Convergence Theory of Generalized Distributed Subgradient Method with Random Quantization—0
HiKonv: Maximizing the Throughput of Quantized Convolution With Novel Bit-wise Management and Computation—0
Auto-regressive Image Synthesis with Integrated Quantization—0
Mixed-Precision Inference Quantization: Radically Towards Faster inference speed, Lower Storage requirement, and Lower Loss—0
Context Unaware Knowledge Distillation for Image RetrievalCode0
Green, Quantized Federated Learning over Wireless Networks: An Energy-Efficient Design—0
RepBNN: towards a precise Binary Neural Network with Enhanced Feature Map via RepeatingCode0
Latent-Domain Predictive Neural Speech Coding—0
FewGAN: Generating from the Joint Distribution of a Few Images—0
Quantized Consensus under Data-Rate Constraints and DoS Attacks: A Zooming-In and Holding Approach—0
Accelerating Deep Learning Model Inference on Arm CPUs with Ultra-Low Bit Quantization and Runtime—0
Is Integer Arithmetic Enough for Deep Learning Training?—0
Optimal Database Allocation in Finite Time with Efficient Communication and Transmission Stopping over Dynamic Networks—0
S4: a High-sparsity, High-performance AI Accelerator—0
Low-bit Shift Network for End-to-End Spoken Language Understanding—0
Semi-supervised Vector-Quantization in Visual SLAM using HGCN—0
T-RECX: Tiny-Resource Efficient Convolutional neural networks with early-eXit—0
DiverGet: A Search-Based Software Testing Approach for Deep Neural Network Quantization Assessment—0
Collaborative Quantization Embeddings for Intra-Subject Prostate MR Image Registration—0
Learning Representations for CSI Adaptive Quantization and Feedback—0
Lipschitz Continuity Retained Binary Neural NetworkCode0
Hybrid Spatial-Temporal Entropy Modelling for Neural Video Compression—0
Sub 8-Bit Quantization of Streaming Keyword Spotting Models for Embedded Chipsets—0
Sparsifying Binary Networks—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