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

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Showing 1001–1050 of 4925 papers

TitleStatusHype
How Does Batch Normalization Help Binary Training?—0
Computation-Efficient Quantization Method for Deep Neural Networks—0
Countering Adversarial Examples: Combining Input Transformation and Noisy Training—0
Covariance Recovery for One-Bit Sampled Data With Time-Varying Sampling Thresholds-Part I: Stationary Signals—0
Covering Numbers for Deep ReLU Networks with Applications to Function Approximation and Nonparametric Regression—0
COVIDLite: A depth-wise separable deep neural network with white balance and CLAHE for detection of COVID-19—0
A Structurally Regularized Convolutional Neural Network for Image Classification using Wavelet-based SubBand Decomposition—0
A Feature-map Discriminant Perspective for Pruning Deep Neural Networks—0
Accurate Block Quantization in LLMs with Outliers—0
CPT-V: A Contrastive Approach to Post-Training Quantization of Vision Transformers—0
3U-EdgeAI: Ultra-Low Memory Training, Ultra-Low BitwidthQuantization, and Ultra-Low Latency Acceleration—0
CQ-VAE: Coordinate Quantized VAE for Uncertainty Estimation with Application to Disk Shape Analysis from Lumbar Spine MRI Images—0
Design Flow of Accelerating Hybrid Extremely Low Bit-width Neural Network in Embedded FPGA—0
CRB Analysis for Mixed-ADC Based DOA Estimation—0
Computational Complexity Evaluation of Neural Network Applications in Signal Processing—0
Croesus: Multi-Stage Processing and Transactions for Video-Analytics in Edge-Cloud Systems—0
Crop Disease Classification using Support Vector Machines with Green Chromatic Coordinate (GCC) and Attention based feature extraction for IoT based Smart Agricultural Applications—0
Cross-Dataset Propensity Estimation for Debiasing Recommender Systems—0
Cross-Layer Discrete Concept Discovery for Interpreting Language Models—0
Cross-Layer Optimization for Fault-Tolerant Deep Learning—0
Cross-Modal Discrete Representation Learning—0
Atleus: Accelerating Transformers on the Edge Enabled by 3D Heterogeneous Manycore Architectures—0
Computability of Classification and Deep Learning: From Theoretical Limits to Practical Feasibility through Quantization—0
A Structurally Regularized CNN Architecture via Adaptive Subband Decomposition—0
Compress, Then Prompt: Improving Accuracy-Efficiency Trade-off of LLM Inference with Transferable Prompt—0
Compress Polyphone Pronunciation Prediction Model with Shared Labels—0
A Fast Network Exploration Strategy to Profile Low Energy Consumption for Keyword Spotting—0
Compressive Spectrum Sensing with 1-bit ADCs—0
Compressive Sensing Using Iterative Hard Thresholding with Low Precision Data Representation: Theory and Applications—0
Compressive Quantization for Fast Object Instance Search in Videos—0
Compressive Estimation of a Stochastic Process with Unknown Autocorrelation Function—0
Associative Memories to Accelerate Approximate Nearest Neighbor Search—0
A Faster Approach to Spiking Deep Convolutional Neural Networks—0
Accuracy to Throughput Trade-offs for Reduced Precision Neural Networks on Reconfigurable Logic—0
Compressive Beam Alignment for Indoor Millimeter-Wave Systems—0
Compression without Quantization—0
A Speed Odyssey for Deployable Quantization of LLMs—0
Compression strategies and space-conscious representations for deep neural networks—0
Adverse Weather Removal with Codebook Priors—0
Compression Scaling Laws:Unifying Sparsity and Quantization—0
Compression-Realized Deep Structural Network for Video Quality Enhancement—0
A SOT-MRAM-based Processing-In-Memory Engine for Highly Compressed DNN Implementation—0
Compression of Site-Specific Deep Neural Networks for Massive MIMO Precoding—0
Compression of Recurrent Neural Networks for Efficient Language Modeling—0
ASI++: Towards Distributionally Balanced End-to-End Generative Retrieval—0
Sparsity Turns Adversarial: Energy and Latency Attacks on Deep Neural Networks—0
3LC: Lightweight and Effective Traffic Compression for Distributed Machine Learning—0
Designing a Classifier for Active Fire Detection from Multispectral Satellite Imagery Using Neural Architecture Search—0
Compression of Generative Pre-trained Language Models via Quantization—0
Compression of Deep Neural Networks on the Fly—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