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 2776–2800 of 4925 papers

TitleStatusHype
Channel-wise Mixed-precision Assignment for DNN Inference on Constrained Edge NodesCode0
Lossy Compression with Gaussian DiffusionCode1
Fast Lossless Neural Compression with Integer-Only Discrete FlowsCode1
tinySNN: Towards Memory- and Energy-Efficient Spiking Neural Networks—0
Accelerating Inference and Language Model Fusion of Recurrent Neural Network Transducers via End-to-End 4-bit Quantization—0
PeQuENet: Perceptual Quality Enhancement of Compressed Video with Adaptation- and Attention-based NetworkCode1
Compressed-VFL: Communication-Efficient Learning with Vertically Partitioned Data—0
Hardening DNNs against Transfer Attacks during Network Compression using Greedy Adversarial PruningCode0
Edge Inference with Fully Differentiable Quantized Mixed Precision Neural Networks—0
QONNX: Representing Arbitrary-Precision Quantized Neural NetworksCode1
Federated Optimization Algorithms with Random Reshuffling and Gradient CompressionCode1
Towards Alternative Techniques for Improving Adversarial Robustness: Analysis of Adversarial Training at a Spectrum of PerturbationsCode0
Optimal Clipping and Magnitude-aware Differentiation for Improved Quantization-aware TrainingCode0
Why Quantization Improves Generalization: NTK of Binary Weight Neural Networks—0
Preprocessing Enhanced Image Compression for Machine Vision—0
A Directed-Evolution Method for Sparsification and Compression of Neural Networks with Application to Object Identification and Segmentation and considerations of optimal quantization using small number of bits—0
Convex Quantization Preserves Logconcavity—0
Real-time Hyper-Dimensional Reconfiguration at the Edge using Hardware Accelerators—0
SDQ: Stochastic Differentiable Quantization with Mixed Precision—0
HideNseek: Federated Lottery Ticket via Server-side Pruning and Sign Supermask—0
Memory-Oriented Design-Space Exploration of Edge-AI Hardware for XR Applications—0
Gradient Obfuscation Gives a False Sense of Security in Federated Learning—0
Low-complexity acoustic scene classification in DCASE 2022 Challenge—0
Enhancing Strong PUF Security with Non-monotonic Response Quantization—0
Learning Binarized Graph Representations with Multi-faceted Quantization Reinforcement for Top-K Recommendation—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