Tips for Optimizing AI Models for Tiny Devices
Complex AI model performance can be computationally and energy intensive. Learn powerful optimization strategies to make your AI models smaller, faster, and more efficient.
Toward a Unified Workflow for Edge AI Deployment
Building an AI model is one thing; delivering it to a constrained device and ensuring it runs reliably is a whole different beast. Learn how to create a single coherent workflow designed for edge deployment.
The Rat Race to MVP in Edge AI: Top 5 Bottlenecks in Edge AI Workflows & How to Overcome Them
Discover the top 5 pitfalls that derail AI-powered solutions, automation, and adoption to different industries and how you can avoid each one.
Optimizing TinyML with Neural Architecture Search: A Practical Guide for Edge AI
Deploying AI onto microcontrollers—known as TinyML—is akin to packing a symphony orchestra inside a wristwatch. Could Neural Architecture Search be the key to achieving this mammoth feat?
Facial Recognition at the Edge is Hard! Imagine it Done with embedUR
Facial recognition is exploding across devices—but deploying it to the edge is brutally hard. Learn why open-source fails, and how embedUR is the closest thing to plug-and-play!
How Neuromorphic Chips Could Redefine Edge AI Devices
Designed to replicate the structure and functionality of the human brain, we take a look at this groundbreaking advancement in computing - Neuromorphic chips
Reducing Energy Demand of AI with Edge Computing
We explore how industries are using energy efficient edge computing to scale AI, improve products and services as well as reduce operational costs.
AI Hardware, Explained
AI hardware refers to the specialized physical components and computational devices, such as TPUs, GPUs and NPUs, that facilitate and accelerate the processing demands of AI algorithms.
Intelligent Edge: A New Era of Network Evolution
The race is on to push machine learning intelligence into small devices, creating the Intelligent Edge. What's involved?