EMC Compliance in Medical Device Design
Oct 24, 2025Explore why EMC compliance is critical for safe, reliable medical devices. Discover how Anders integrates Design…
Read moreChoosing the Best Hardware for Deep Learning
In today's fast-paced world of machine learning, neural networks have become more computationally intensive, making Machine Learning (ML) implementation on embedded systems increasingly challenging. This paper explores a comparative analysis of Anders embedded platforms and third-party accelerators, focusing on the efficiency, performance and cost of deploying the YOLOv5s model - a renowned deep learning model for object detection on embedded platforms.
It compares CPUs, ARM architectures, and AI accelerators including Coral TPU and Hailo-8, offering insights for optimising ML deployments.
Complete the form below to download the guide
