MobileNetで使われるDepthwise Separable Convolutionについて、簡単な例を用いて解説します。 Depthwise Separable Convolutionとは？ Depthwise Separable Convolutionは、通常の畳み込み演算を2つのステップに分解することで、計算量を大幅に減らす手法です。 1. 通常の畳み込み（例 ...

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 ...

・畳み込み層の基礎的な知識を理解する ・全結合層と畳み込み層の差異について、説明できる ・畳み込み層の役割について説明できる ・畳み込み層のパラメータ数について理解する ・畳み込み層が適用できるデータの特性について理解する 直訳すると ...

This repository contains the hardware design, software references, and lab documentation for EE310 Lab 4. The project develops parameterizable depthwise, pointwise, and regular 2D convolution blocks ...

Abstract: Computing-in-Memory (CIM) is a promising solution to address the bottleneck of data movement in traditional Von Neumann architecture by performing in-situ computation in the memory. However, ...

Convolution and transposed convolution (often referred to as deconvolution) are fundamental operations in deep neural networks. Convolution is commonly used for feature extraction and spatial ...