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Integer-Only CNNs with 4 Bit Weights and Bit-Shift Quantization Scales at Full-Precision Accuracy

https://doi.org/10.3390/electronics10222823

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Value-Aware Quantization for Training and Inference of Neural Networks

inferredcites →https://doi.org/10.1007/978-3-030-01237-3_23openalex-snapshot

LQ-Nets: Learned Quantization for Highly Accurate and Compact Deep Neural Networks

inferredcites →https://doi.org/10.1007/978-3-030-58536-5_5openalex-snapshot

Post-training Piecewise Linear Quantization for Deep Neural Networks

inferredcites →https://doi.org/10.1007/978-3-030-58539-6_26openalex-snapshot

PROFIT: A Novel Training Method for sub-4-bit MobileNet Models

inferredcites →https://doi.org/10.1007/978-3-319-46493-0_32openalex-snapshot

XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks

inferredcites →https://doi.org/10.1016/0893-6080(91)90077-iopenalex-snapshot

Weight quantization in Boltzmann machines

inferredcites →https://doi.org/10.1109/acssc.2017.8335699openalex-snapshot

Minimum energy quantized neural networks

inferredcites →https://doi.org/10.1109/cvpr.2009.5206848openalex-snapshot

ImageNet: A large-scale hierarchical image database

inferredcites →https://doi.org/10.1109/cvpr.2016.308openalex-snapshot

Rethinking the Inception Architecture for Computer Vision

inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot

Deep Residual Learning for Image Recognition

inferredcites →https://doi.org/10.1109/cvpr.2017.690openalex-snapshot

YOLO9000: Better, Faster, Stronger

inferredcites →https://doi.org/10.1109/cvpr.2018.00286openalex-snapshot

Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference

inferredcites →https://doi.org/10.1109/cvpr.2018.00474openalex-snapshot

MobileNetV2: Inverted Residuals and Linear Bottlenecks

inferredcites →https://doi.org/10.1109/cvpr.2018.00826openalex-snapshot

Towards Effective Low-Bitwidth Convolutional Neural Networks

inferredcites →https://doi.org/10.1109/cvpr.2019.00292openalex-snapshot

Fully Quantized Network for Object Detection

inferredcites →https://doi.org/10.1109/cvpr.2019.00748openalex-snapshot

Quantization Networks

inferredcites →https://doi.org/10.1109/cvpr42600.2020.00242openalex-snapshot

Rethinking Differentiable Search for Mixed-Precision Neural Networks

inferredcites →https://doi.org/10.1109/cvpr42600.2020.01318openalex-snapshot

ZeroQ: A Novel Zero Shot Quantization Framework

inferredcites →https://doi.org/10.1109/iccv.2019.00038openalex-snapshot

HAWQ: Hessian AWare Quantization of Neural Networks With Mixed-Precision

inferredcites →https://doi.org/10.1109/tvlsi.2019.2939429openalex-snapshot

AddNet: Deep Neural Networks Using FPGA-Optimized Multipliers

inferredcites →https://doi.org/10.24963/ijcai.2019/425openalex-snapshot

Learning Low-precision Neural Networks without Straight-Through Estimator (STE)

inferredcites →https://doi.org/10.48550/arxiv.1409.1556openalex-snapshot

Very Deep Convolutional Networks for Large-Scale Image Recognition

inferredcites →https://doi.org/10.48550/arxiv.1502.03167openalex-snapshot

Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

inferredcites →https://doi.org/10.48550/arxiv.1702.03044openalex-snapshot

Incremental Network Quantization: Towards Lossless CNNs with Low-Precision Weights

inferredcites →https://doi.org/10.48550/arxiv.1909.13144openalex-snapshot

Additive Powers-of-Two Quantization: An Efficient Non-uniform Discretization for Neural Networks

inferredcites →https://doi.org/10.48550/arxiv.2004.10568openalex-snapshot

Up or Down? Adaptive Rounding for Post-Training Quantization

inferredcites →https://doi.org/10.48550/arxiv.2010.11267openalex-snapshot

MicroNets: Neural Network Architectures for Deploying TinyML Applications on Commodity Microcontrollers

inferredcites →https://doi.org/10.5281/zenodo.3461979openalex-snapshot

Fast Adjustable Threshold for Uniform Neural Network Quantization

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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IE-Net: Information-Enhanced Binary Neural Networks for Accurate Classification

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Quantized Semantic Segmentation Deep Architecture for Deployment on an Edge Computing Device for Image Segmentation

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Efficient Deep Learning Model Compression for Sensor-Based Vision Systems via Outlier-Aware Quantization

inferred← citeshttps://doi.org/10.36227/techrxiv.21687248openalex-snapshot

A Review of AIoT-based Edge Devices and Lightweight Deployment

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A Comprehensive Review of AIoT-based Edge Devices and Lightweight Deployment

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A Review of AIoT-based Edge Devices and Lightweight Deployment

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An EEG-based Multi-Time Scale Spatiotemporal Compound Model for Assessment of Teaching Effectiveness in Navigation Course

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