AI In the Cloud Vs. On the Hardware: Where Tiny ML Shines
AI In the Cloud Vs. On the Hardware: Where Tiny ML Shines 
AI in the Cloud vs. On Hardware: A Deep Dive into TinyML with Anders Hardebring
On this episode of the eeDesignIt Podcast, we dive into the differences between AI in the cloud vs. on hardware with a discussion about TinyML and the serious advances in the space.
Tiny Machine Learning (TinyML) is broadly defined as a fast-growing field of machine learning technologies and applications, including hardware (dedicated integrated circuits), algorithms, and software capable of performing on-device sensor data analytics (vision, audio, IMU, biomedical, etc.) at extremely low power—typically in the mW range and below. This enables a variety of always-on use cases, targeting battery-operated devices.
Anders Hardebring, CEO and Co-Founder of Imagimob, is helping companies imagine what they can do by bringing machine learning into embedded devices. Imagine audio classification without any data ever leaving the embedded device. Or sensing that a machine is about to break and turning it off in just a few milliseconds, even when the connection is down.
Tune in for a deep dive into the possibilities and explore how TinyML is revolutionizing embedded systems and AI. Be sure to visit Imagimob to learn more.
