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

Showing 27512800 of 4925 papers

TitleStatusHype
BL-JUNIPER: A CNN-Assisted Framework for Perceptual Video Coding Leveraging Block-Level JNDCode1
Supervised Deep Hashing for High-dimensional and Heterogeneous Case-based Reasoning0
Multi-user Downlink Beamforming using Uplink Downlink Duality with 1-bit Converters for Flat Fading Channels0
Fundamental Limits of Communication Efficiency for Model Aggregation in Distributed Learning: A Rate-Distortion Approach0
QTI Submission to DCASE 2021: residual normalization for device-imbalanced acoustic scene classification with efficient design0
CTMQ: Cyclic Training of Convolutional Neural Networks with Multiple Quantization Steps0
Cactus Mechanisms: Optimal Differential Privacy Mechanisms in the Large-Composition Regime0
Inverted Semantic-Index for Image Retrieval0
Computational Complexity Evaluation of Neural Network Applications in Signal Processing0
Megapixel Image Generation with Step-Unrolled Denoising AutoencodersCode0
QReg: On Regularization Effects of Quantization0
Open-source FPGA-ML codesign for the MLPerf Tiny BenchmarkCode0
A Federated Reinforcement Learning Method with Quantization for Cooperative Edge Caching in Fog Radio Access Networks0
Automated Cancer Subtyping via Vector Quantization Mutual Information MaximizationCode0
Proximity Graph Maintenance for Fast Online Nearest Neighbor Search0
Quantization Robust Federated Learning for Efficient Inference on Heterogeneous Devices0
Asymmetric Learned Image Compression with Multi-Scale Residual Block, Importance Map, and Post-Quantization Filtering0
QuantFace: Towards Lightweight Face Recognition by Synthetic Data Low-bit QuantizationCode1
sqSGD: Locally Private and Communication Efficient Federated Learning0
Self-Supervised Consistent Quantization for Fully Unsupervised Image Retrieval0
Low-Precision Stochastic Gradient Langevin DynamicsCode0
LUT-GEMM: Quantized Matrix Multiplication based on LUTs for Efficient Inference in Large-Scale Generative Language ModelsCode1
Seizure Detection and Prediction by Parallel Memristive Convolutional Neural NetworksCode1
Towards Efficient Active Learning of PDFACode0
FedNew: A Communication-Efficient and Privacy-Preserving Newton-Type Method for Federated LearningCode1
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 Networks0
Accelerating Inference and Language Model Fusion of Recurrent Neural Network Transducers via End-to-End 4-bit Quantization0
PeQuENet: Perceptual Quality Enhancement of Compressed Video with Adaptation- and Attention-based NetworkCode1
Compressed-VFL: Communication-Efficient Learning with Vertically Partitioned Data0
Hardening DNNs against Transfer Attacks during Network Compression using Greedy Adversarial PruningCode0
Edge Inference with Fully Differentiable Quantized Mixed Precision Neural Networks0
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 Networks0
Preprocessing Enhanced Image Compression for Machine Vision0
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 bits0
Convex Quantization Preserves Logconcavity0
Real-time Hyper-Dimensional Reconfiguration at the Edge using Hardware Accelerators0
SDQ: Stochastic Differentiable Quantization with Mixed Precision0
HideNseek: Federated Lottery Ticket via Server-side Pruning and Sign Supermask0
Memory-Oriented Design-Space Exploration of Edge-AI Hardware for XR Applications0
Gradient Obfuscation Gives a False Sense of Security in Federated Learning0
Low-complexity acoustic scene classification in DCASE 2022 Challenge0
Enhancing Strong PUF Security with Non-monotonic Response Quantization0
Learning Binarized Graph Representations with Multi-faceted Quantization Reinforcement for Top-K Recommendation0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1FQ-ViT (ViT-L)Top-1 Accuracy (%)85.03Unverified
2FQ-ViT (ViT-B)Top-1 Accuracy (%)83.31Unverified
3FQ-ViT (Swin-B)Top-1 Accuracy (%)82.97Unverified
4FQ-ViT (Swin-S)Top-1 Accuracy (%)82.71Unverified
5FQ-ViT (DeiT-B)Top-1 Accuracy (%)81.2Unverified
6FQ-ViT (Swin-T)Top-1 Accuracy (%)80.51Unverified
7FQ-ViT (DeiT-S)Top-1 Accuracy (%)79.17Unverified
8Xception W8A8Top-1 Accuracy (%)78.97Unverified
9ADLIK-MO-ResNet50-W4A4Top-1 Accuracy (%)77.88Unverified
10ADLIK-MO-ResNet50-W3A4Top-1 Accuracy (%)77.34Unverified
#ModelMetricClaimedVerifiedStatus
13DCNN_VIVA_3MAP160,327.04Unverified
2DTQMAP0.79Unverified
#ModelMetricClaimedVerifiedStatus
1OutEffHop-Bert_basePerplexity6.3Unverified
2OutEffHop-Bert_basePerplexity6.21Unverified
#ModelMetricClaimedVerifiedStatus
1Accuracy98.13Unverified
#ModelMetricClaimedVerifiedStatus
1Accuracy92.92Unverified
#ModelMetricClaimedVerifiedStatus
1SSD ResNet50 V1 FPN 640x640MAP34.3Unverified
#ModelMetricClaimedVerifiedStatus
1TAR @ FAR=1e-495.13Unverified
#ModelMetricClaimedVerifiedStatus
1TAR @ FAR=1e-496.38Unverified
#ModelMetricClaimedVerifiedStatus
13DCNN_VIVA_5All84,809,664Unverified
#ModelMetricClaimedVerifiedStatus
1Accuracy99.8Unverified