Many people have begun experimenting with using machine learning in embedded systems as the two technologies have become more prominent in today’s society. That approach allows for overcoming many of ...

Creating accurate inference models is of course the payoff to a supervised learning process able to draw on a wide range of underlying model types and architectures. Among these model types, neural ...

Deciding on the programming language for your next embedded product may not be as simple as just choosing C. While C has been the industry's go-to workhorse for the past 50 years, its features and ...

Why it’s important not to over-engineer. Equipped with suitable hardware, IDEs, development tools and kits, frameworks, datasets, and open-source models, engineers can develop ML/AI-enabled, ...

Embedded-systems designers are on a mission to squeeze powerful AI algorithms into resource-constrained gadgets, relying on cutting-edge custom hardware accelerators and high-level synthesis to push ...

Harvard School of Engineering and Applied Sciences offers Fundamentals of TinyML as an introductory online course through its ...

Embedded platforms have become an integral part of our daily lives, revolutionizing our technological interaction. These platforms, equipped with deep learning algorithms, have opened a world of ...

In chilly economic climes, it’s more important than ever, according to the experts, to embed a learning culture in an organisation. But how can this be done? Achieving an embedded learning culture is ...

At the start of the year, I highlighted key trends in 5 Embedded Software Trends to Watch in 2024, predicting the forces shaping the industry. As the year unfolded, many of these trends indeed drove ...