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 3401–3425 of 4925 papers

TitleStatusHype
Model Selection CNN-based VVC QualityEnhancement—0
Q-Rater: Non-Convex Optimization for Post-Training Uniform Quantization—0
A reconfigurable neural network ASIC for detector front-end data compression at the HL-LHC—0
One Model for All Quantization: A Quantized Network Supporting Hot-Swap Bit-Width Adjustment—0
Training Quantized Neural Networks to Global Optimality via Semidefinite Programming—0
Binarized Aggregated Network with Quantization: Flexible Deep Learning Deployment for CSI Feedback in Massive MIMO SystemCode1
On the Adversarial Robustness of Quantized Neural Networks—0
Stealthy Backdoors as Compression ArtifactsCode0
Memory-Efficient Deep Learning Inference in Trusted Execution Environments—0
Hessian Aware Quantization of Spiking Neural NetworksCode0
ActNN: Reducing Training Memory Footprint via 2-Bit Activation Compressed TrainingCode1
NUQSGD: Provably Communication-efficient Data-parallel SGD via Nonuniform Quantization—0
HAO: Hardware-aware neural Architecture Optimization for Efficient Inference—0
Spatio-Temporal Pruning and Quantization for Low-latency Spiking Neural Networks—0
Quantization of Deep Neural Networks for Accurate Edge Computing—0
Do All MobileNets Quantize Poorly? Gaining Insights into the Effect of Quantization on Depthwise Separable Convolutional Networks Through the Eyes of Multi-scale Distributional Dynamics—0
Differentiable Model Compression via Pseudo Quantization NoiseCode1
FPGA Implementations of Layered MinSum LDPC Decoders Using RCQ Message Passing—0
Conditional Coding and Variable Bitrate for Practical Learned Video CodingCode1
Filtering Empty Camera Trap Images in Embedded SystemsCode0
Random and Adversarial Bit Error Robustness: Energy-Efficient and Secure DNN AcceleratorsCode0
Matching-oriented Product Quantization For Ad-hoc RetrievalCode1
Homomorphic Encryption-Enabled Distance-Based Distributed Formation Control with Distance Mismatch Estimators—0
Span Pointer Networks for Non-Autoregressive Task-Oriented Semantic Parsing—0
End-to-end Keyword Spotting using Neural Architecture Search and 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