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 4101–4125 of 4925 papers

TitleStatusHype
On Neural Architecture Search for Resource-Constrained Hardware Platforms—0
SPARQ-SGD: Event-Triggered and Compressed Communication in Decentralized Stochastic Optimization—0
Exploiting Intelligent Reflecting Surfaces in NOMA Networks: Joint Beamforming Optimization—0
Channel Estimation for MIMO Hybrid Architectures with Low Resolution ADCs for mmWave Communication—0
Training DNN IoT Applications for Deployment On Analog NVM Crossbars—0
Integrating PHY Security Into NDN-IoT Networks By Exploiting MEC: Authentication Efficiency, Robustness, and Accuracy Enhancement—0
Noiseless Privacy—0
Towards Unsupervised Speech Recognition and Synthesis with Quantized Speech Representation Learning—0
Secure Evaluation of Quantized Neural Networks—0
Asynchronous Decentralized SGD with Quantized and Local Updates—0
A holistic approach to polyphonic music transcription with neural networksCode1
CNN-based Analog CSI Feedback in FDD MIMO-OFDM Systems—0
Q-GADMM: Quantized Group ADMM for Communication Efficient Decentralized Machine Learning—0
A Binary Variational Autoencoder for HashingCode0
Image processing in DNA—0
Mirror Descent View for Neural Network QuantizationCode0
Fully Quantized Transformer for Machine Translation—0
Reinforced Bit Allocation under Task-Driven Semantic Distortion Metrics—0
Parametric context adaptive Laplace distribution for multimedia compression—0
Variation-aware Binarized Memristive Networks—0
Q8BERT: Quantized 8Bit BERTCode1
Automatic Neural Network Compression by Sparsity-Quantization Joint Learning: A Constrained Optimization-based ApproachCode0
OverQ: Opportunistic Outlier Quantization for Neural Network Accelerators—0
QPyTorch: A Low-Precision Arithmetic Simulation FrameworkCode0
High-Dimensional Stochastic Gradient Quantization for Communication-Efficient Edge Learning—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