Tensor processing units (TPUs) are one of the most well-known machine learning (ML)
accelerators utilized at large scale in data centers as well as in tiny ML applications. TPUs …

This paper explores the synergistic potential of neuromorphic and edge computing to create
a versatile machine learning (ML) system tailored for processing data captured by dynamic …

Methods from Machine Learning (ML) and Computer Vision (CV) have proven powerful tools
for quickly and accurately analyzing behavioral recordings. The computational complexity of …

Pathological alterations in the human vascular system underlie many chronic diseases, such
as atherosclerosis and aneurysms. However, manually analyzing diagnostic images of the …

This paper explores the problem of deploying machine learning (ML)-based object detection
and segmentation models on edge platforms to enable realtime caveline detection for …

This paper addresses the challenge of deploying machine learning (ML)-based
segmentation models on edge platforms to facilitate real-time scene segmentation for …

The paper focuses on real-time facial expression recognition (FER) systems as an important
component in various real-world applications such as social robotics. We investigate two …

Abstract Machine learning (ML) has become ubiquitous, integrating into numerous real-life
applications. However, meeting the computational demands of ML systems is challenging …

In the field of video processing, edge detection plays a crucial role in various applications
such as object recognition, scene understanding, and video surveillance. Real-time …

With the rapid advancements in deep learning models over the last decade, artificial
intelligence applications such as object detection, object tracking, and audio processing and …