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 851900 of 4925 papers

TitleStatusHype
Hierarchical Quantized AutoencodersCode1
Algorithm-hardware Co-design for Deformable ConvolutionCode1
Robust Quantization: One Model to Rule Them AllCode1
Variational Bayesian QuantizationCode1
Learning Architectures for Binary NetworksCode1
Precision Gating: Improving Neural Network Efficiency with Dynamic Dual-Precision ActivationsCode1
Towards Sharper First-Order Adversary with Quantized GradientsCode1
Post-Training Piecewise Linear Quantization for Deep Neural NetworksCode1
Fast, Compact and Highly Scalable Visual Place Recognition through Sequence-based Matching of Overloaded RepresentationsCode1
Least squares binary quantization of neural networksCode1
Fractional Skipping: Towards Finer-Grained Dynamic CNN InferenceCode1
Towards Accurate Post-training Network Quantization via Bit-Split and StitchingCode1
ZeroQ: A Novel Zero Shot Quantization FrameworkCode1
Towards Efficient Training for Neural Network QuantizationCode1
MetaQuant: Learning to Quantize by Learning to Penetrate Non-differentiable QuantizationCode1
Online Learned Continual Compression with Adaptive Quantization ModulesCode1
Scientific Image Restoration AnywhereCode1
HAWQ-V2: Hessian Aware trace-Weighted Quantization of Neural NetworksCode1
Effectiveness of self-supervised pre-training for speech recognitionCode1
ConveRT: Efficient and Accurate Conversational Representations from TransformersCode1
A holistic approach to polyphonic music transcription with neural networksCode1
Q8BERT: Quantized 8Bit BERTCode1
Improvements to Target-Based 3D LiDAR to Camera CalibrationCode1
And the Bit Goes Down: Revisiting the Quantization of Neural NetworksCode1
Data-Free Quantization Through Weight Equalization and Bias CorrectionCode1
Mixed Precision DNNs: All you need is a good parametrizationCode1
Learning to Groove with Inverse Sequence TransformationsCode1
HAWQ: Hessian AWare Quantization of Neural Networks with Mixed-PrecisionCode1
Single Path One-Shot Neural Architecture Search with Uniform SamplingCode1
Learned Step Size QuantizationCode1
ADMM-NN: An Algorithm-Hardware Co-Design Framework of DNNs Using Alternating Direction Method of MultipliersCode1
Proximal Mean-field for Neural Network QuantizationCode1
Trained Rank Pruning for Efficient Deep Neural NetworksCode1
NICE: Noise Injection and Clamping Estimation for Neural Network QuantizationCode1
PACT: Parameterized Clipping Activation for Quantized Neural NetworksCode1
Vision Based Dynamic Offside Line Marker for Soccer GamesCode1
Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only InferenceCode1
Sparse Communication for Distributed Gradient DescentCode1
FastText.zip: Compressing text classification modelsCode1
Trained Ternary QuantizationCode1
Efficient Deployment of Spiking Neural Networks on SpiNNaker2 for DVS Gesture Recognition Using Neuromorphic Intermediate RepresentationCode0
An End-to-End DNN Inference Framework for the SpiNNaker2 Neuromorphic MPSoC0
Task-Specific Audio Coding for Machines: Machine-Learned Latent Features Are Codes for That Machine0
Angle Estimation of a Single Source with Massive Uniform Circular Arrays0
Quantized Rank Reduction: A Communications-Efficient Federated Learning Scheme for Network-Critical Applications0
Lightweight Federated Learning over Wireless Edge Networks0
Vision Foundation Models as Effective Visual Tokenizers for Autoregressive Image Generation0
OpenDPDv2: A Unified Learning and Optimization Framework for Neural Network Digital Predistortion0
QS4D: Quantization-aware training for efficient hardware deployment of structured state-space sequential models0
Semantic Certainty Assessment in Vector Retrieval Systems: A Novel Framework for Embedding Quality Evaluation0
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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