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 41514200 of 4925 papers

TitleStatusHype
QuaRL: Quantization for Fast and Environmentally Sustainable Reinforcement LearningCode0
Hierarchical Encoding of Sequential Data With Compact and Sub-Linear Storage CostCode0
NGEMM: Optimizing GEMM for Deep Learning via Compiler-based Techniques0
DSConv: Efficient Convolution Operator0
Automated design of error-resilient and hardware-efficient deep neural networks0
XNOR-Net++: Improved Binary Neural Networks0
Optimal Controller and Quantizer Selection for Partially Observable Linear-Quadratic-Gaussian Systems0
AdaptivFloat: A Floating-point based Data Type for Resilient Deep Learning Inference0
REQ-YOLO: A Resource-Aware, Efficient Quantization Framework for Object Detection on FPGAs0
Additive Powers-of-Two Quantization: An Efficient Non-uniform Discretization for Neural NetworksCode0
FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization0
Optimized Quantization in Distributed Graph Signal Filtering0
Impact of Low-bitwidth Quantization on the Adversarial Robustness for Embedded Neural Networks0
GA-GAN: CT reconstruction from Biplanar DRRs using GAN with Guided Attention0
Optimizing Speech Recognition For The Edge0
Adaptive Binary-Ternary Quantization0
Goten: GPU-Outsourcing Trusted Execution of Neural Network Training and PredictionCode0
Hybrid Weight Representation: A Quantization Method Represented with Ternary and Sparse-Large Weights0
QGAN: Quantize Generative Adversarial Networks to Extreme low-bits0
Rethinking Neural Network Quantization0
Prune or quantize? Strategy for Pareto-optimally low-cost and accurate CNN0
Provably Communication-efficient Data-parallel SGD via Nonuniform Quantization0
Smart Ternary Quantization0
Lattice Representation Learning0
CURSOR-BASED ADAPTIVE QUANTIZATION FOR DEEP NEURAL NETWORK0
Learning Compact Embedding Layers via Differentiable Product Quantization0
Compression without Quantization0
Learning to Seek: Autonomous Source Seeking with Deep Reinforcement Learning Onboard a Nano Drone MicrocontrollerCode0
CAT: Compression-Aware Training for bandwidth reductionCode0
Low Rank Training of Deep Neural Networks for Emerging Memory Technology0
Towards Effective 2-bit Quantization: Pareto-optimal Bit Allocation for Deep CNNs Compression0
OPTIMAL BINARY QUANTIZATION FOR DEEP NEURAL NETWORKS0
On the Pareto Efficiency of Quantized CNN0
Online Learned Continual Compression with Stacked Quantization Modules0
Monte Carlo Deep Neural Network Arithmetic0
Network Pruning for Low-Rank Binary Index0
GQ-Net: Training Quantization-Friendly Deep Networks0
Forward and Backward Information Retention for Accurate Binary Neural NetworksCode0
Autoencoder-Based Error Correction Coding for One-Bit Quantization0
A System-Level Solution for Low-Power Object Detection0
Gridless Angular Domain Channel Estimation for mmWave Massive MIMO System With One-Bit Quantization Via Approximate Message Passing0
Structured Binary Neural Networks for Image Recognition0
HAWKEYE: Adversarial Example Detector for Deep Neural Networks0
How to design a derivatives market?0
Performance Analysis of Massive MIMO Multi-Way Relay Networks with Low-Resolution ADCs0
How Does Batch Normalization Help Binary Training?0
Fast Large-Scale Discrete Optimization Based on Principal Coordinate Descent0
An Empirical Study towards Characterizing Deep Learning Development and Deployment across Different Frameworks and Platforms0
DASNet: Dynamic Activation Sparsity for Neural Network Efficiency Improvement0
Neural Machine Translation with 4-Bit Precision and Beyond0
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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