IEEE

Depthwise Separable Convolution architectures for 3D Object Recognition *

Abstract: Fast and efficient recognition of 3D objects remains vital in computer vision. View-based recognition has gained increasing attention because it mimics human perception by recognizing ...
IEEE

MixCIM: A Hybrid Computing-in-Memory Macro With Less Data-Movement and Better Memory-Reuse for Depthwise Separable Neural Networks

Abstract: Computing-in-memory (CIM) architectures have demonstrated strong potential for edge artificial intelligence (AI) devices due to their enhanced parallelism and energy efficiency. With the ...
GitHub

depthwise_separable_conv_module.py

See https://arxiv.org/pdf/1704.04861.pdf for details. This module can replace a ConvModule with the conv block replaced by two conv block: depthwise conv block and ...
GitHub

ZaGrayWolf/Types_of_Convolutions

Traditional convolution is the foundation of convolutional neural networks (CNNs). It involves sliding a set of learnable filters (kernels) over the input data (e.g., an image) to generate feature ...