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
Compression without Quantization0
Rethinking Neural Network Quantization0
Online Learned Continual Compression with Stacked Quantization Modules0
Smart Ternary Quantization0
QGAN: Quantize Generative Adversarial Networks to Extreme low-bits0
Network Pruning for Low-Rank Binary Index0
CURSOR-BASED ADAPTIVE QUANTIZATION FOR DEEP NEURAL NETWORK0
OPTIMAL BINARY QUANTIZATION FOR DEEP NEURAL NETWORKS0
Low Rank Training of Deep Neural Networks for Emerging Memory Technology0
GQ-Net: Training Quantization-Friendly Deep Networks0
Learning Compact Embedding Layers via Differentiable Product Quantization0
Lattice Representation Learning0
Provably Communication-efficient Data-parallel SGD via Nonuniform Quantization0
CAT: Compression-Aware Training for bandwidth reductionCode0
Learning to Seek: Autonomous Source Seeking with Deep Reinforcement Learning Onboard a Nano Drone MicrocontrollerCode0
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
HAWKEYE: Adversarial Example Detector for Deep Neural Networks0
Structured Binary Neural Networks for Image Recognition0
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
Q-BERT: Hessian Based Ultra Low Precision Quantization of BERT0
Variable Rate Deep Image Compression With a Conditional Autoencoder0
Communication-Censored Distributed Stochastic Gradient DescentCode0
Sparse linear regression with compressed and low-precision data via concave quadratic programming0
A Comprehensive Benchmark for Single Image Compression Artifacts Reduction0
A Resource-Efficient Embedded Iris Recognition System Using Fully Convolutional NetworksCode0
Restricted Minimum Error Entropy Criterion for Robust Classification0
Quantized Fisher Discriminant AnalysisCode0
DeepEvolution: A Search-Based Testing Approach for Deep Neural Networks0
Training High-Performance and Large-Scale Deep Neural Networks with Full 8-bit IntegersCode0
Symbol-Level Precoding Design for Intelligent Reflecting Surface Assisted Multi-user MIMO Systems0
On the Downstream Performance of Compressed Word EmbeddingsCode0
Push for Quantization: Deep Fisher Hashing0
PULP-NN: Accelerating Quantized Neural Networks on Parallel Ultra-Low-Power RISC-V ProcessorsCode0
An Ultra-Efficient Memristor-Based DNN Framework with Structured Weight Pruning and Quantization Using ADMM0
Smaller Models, Better Generalization0
Accelerating Large-Scale Inference with Anisotropic Vector QuantizationCode0
Tiny but Accurate: A Pruned, Quantized and Optimized Memristor Crossbar Framework for Ultra Efficient DNN Implementation0
Differentiable Product Quantization for End-to-End Embedding CompressionCode0
Incremental Binarization On Recurrent Neural Networks For Single-Channel Source Separation0
RATQ: A Universal Fixed-Length Quantizer for Stochastic Optimization0
A Gridless Compressive Sensing Based Channel Estimation for Millimeter Wave MIMO OFDM Systems with One-Bit Quantization0
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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