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 4051–4100 of 4925 papers

TitleStatusHype
Embedding Compression with Isotropic Iterative Quantization—0
Gaussian Approximation of Quantization Error for Estimation from Compressed Data—0
Resource-Efficient Neural Networks for Embedded Systems—0
RPR: Random Partition Relaxation for Training; Binary and Ternary Weight Neural Networks—0
Attention based on-device streaming speech recognition with large speech corpus—0
Don't Waste Your Bits! Squeeze Activations and Gradients for Deep Neural Networks via TinyScript—0
Acceleration for Compressed Gradient Descent in Distributed Optimization—0
Train Big, Then Compress: Rethinking Model Size for Efficient Training and Inference of Transformers—0
Differentiable Product Quantization for Learning Compact Embedding Layers—0
Efficient Systolic Array Based on Decomposable MAC for Quantized Deep Neural Networks—0
New Loss Functions for Fast Maximum Inner Product Search—0
Mixed-Precision Quantized Neural Network with Progressively Decreasing Bitwidth For Image Classification and Object Detection—0
Towards Unified INT8 Training for Convolutional Neural Network—0
AdaBits: Neural Network Quantization with Adaptive Bit-WidthsCode0
EAST: Encoding-Aware Sparse Training for Deep Memory Compression of ConvNetsCode0
FQ-Conv: Fully Quantized Convolution for Efficient and Accurate Inference—0
Interleaved Composite Quantization for High-Dimensional Similarity Search—0
Adaptive Loss-aware Quantization for Multi-bit NetworksCode0
Neural Networks Weights Quantization: Target None-retraining Ternary (TNT)—0
Efficient Error-Tolerant Quantized Neural Network Accelerators—0
Attention network forecasts time-to-failure in laboratory shear experiments—0
Learned Variable-Rate Image Compression with Residual Divisive Normalization—0
Maximum Average Entropy-Based Quantization of Local Observations for Distributed Detection—0
Compressing 3DCNNs Based on Tensor Train Decomposition—0
Tensor Recovery from Noisy and Multi-Level Quantized Measurements—0
RTN: Reparameterized Ternary Network—0
Deep Model Compression Via Two-Stage Deep Reinforcement Learning—0
EDAS: Efficient and Differentiable Architecture Search—0
Optimizing the energy consumption of spiking neural networks for neuromorphic applicationsCode0
Coresets for Archetypal AnalysisCode0
Generalization Error Analysis of Quantized Compressive Learning—0
Error Correcting Output Codes Improve Probability Estimation and Adversarial Robustness of Deep Neural NetworksCode0
Post training 4-bit quantization of convolutional networks for rapid-deploymentCode0
Normalization Helps Training of Quantized LSTMCode0
The Synthesis of XNOR Recurrent Neural Networks with Stochastic Logic—0
Random Projections with Asymmetric Quantization—0
Qsparse-local-SGD: Distributed SGD with Quantization, Sparsification and Local ComputationsCode0
A binary-activation, multi-level weight RNN and training algorithm for ADC-/DAC-free and noise-resilient processing-in-memory inference with eNVM—0
Semi-Relaxed Quantization with DropBits: Training Low-Bit Neural Networks via Bit-wise Regularization—0
QKD: Quantization-aware Knowledge Distillation—0
Neural Network-Inspired Analog-to-Digital Conversion to Achieve Super-Resolution with Low-Precision RRAM Devices—0
Music Source Separation in the Waveform Domain—0
Two-Stage Learning for Uplink Channel Estimation in One-Bit Massive MIMO—0
Model-Aware Deep Architectures for One-Bit Compressive Variational AutoencodingCode0
Pyramid Vector Quantization and Bit Level Sparsity in Weights for Efficient Neural Networks Inference—0
A SOT-MRAM-based Processing-In-Memory Engine for Highly Compressed DNN Implementation—0
Quantization NetworksCode0
Learning Hierarchical Discrete Linguistic Units from Visually-Grounded Speech—0
IFQ-Net: Integrated Fixed-point Quantization Networks for Embedded Vision—0
On the Discrepancy between the Theoretical Analysis and Practical Implementations of Compressed Communication for Distributed Deep LearningCode0
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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