Scientific Research Publishing

Geo-Refined Point Transformer: Coordinate-Aware Excitation and Positional Upsampling for 3D Scene Segmentation ()

The proposed Coordinate-Aware Feature Excitation (CAFE) module and Position-Aware Upsampling (Pos-Up) module both adhere to ...
IEEE

Low-Overhead CNN Acceleration via FFT-based Circular Convolution and Partially Pipelined Dataflow

Abstract: Fast Fourier Transformation (FFT) has been widely recognized as an effective method for reducing the computational density of convolutional neural networks (CNNs). However, existing ...
GitHub

About the motivation of Interactive Convolution Block

Hi, thank you for sharing the code. Regarding the Interactive Convolution Block, it is written in the paper: ``The element-wise multiplication encourages interactions between features extracted at ...
GitHub

NXP Backend: Quantize selected activation functions with Conv/Matrix multiplication

module: nxpIssues related to NXP Neutron NPU delegation and code under backends/nxp/Issues related to NXP Neutron NPU delegation and code under backends/nxp/ Currently the Neutron Quantizer, quantizes ...
Frontiers

Design of a hybrid AI network circuit for epilepsy detection with 97.5% accuracy and low cost-latency

Epilepsy detection using artificial intelligence (AI) networks has gained significant attention. However, existing methods face challenges in accuracy, computational cost, and speed. CNN excel in ...
Frontiers

Evaluation of Trident network against atlas and 2D U-net methods for auto-segmentation of organs at risk in nasopharyngeal carcinoma: a comparative study

Background and objective(s): This study evaluates the accuracy of automatic segmentation of Organs at Risk (OARs) in nasopharyngeal carcinoma (NPC) using three approaches: atlas-based, 2D U-Net, and a ...
Ars Technica

Researchers upend AI status quo by eliminating matrix multiplication in LLMs

Researchers claim to have developed a new way to run AI language models more efficiently by eliminating matrix multiplication from the process. This fundamentally redesigns neural network operations ...