What if you threw a party but half the guests of honor never showed? That was the situation at the tinyML Summit that took place in San Francisco earlier this week. Together, chip vendors, startups trying to shrink machine learning (ML) algorithms, … [Read more...]
How can we make TinyML secure? (And why we need to)
Lately, I've been reporting more and more on algorithms that parse incoming data locally in order to make some sort of decision. Last week, for example, I wrote about a company doing voice analysis locally to detect Alzheimer's. I've also covered … [Read more...]
Podcast: Smart home improvement is now a thing
This week's show starts with a healthy portion of chips, with the main course being Nvidia's reported acceptance that its deal to acquire ARM isn't likely to happen. We then turn to the U.S. Commerce Department's plans to combat the chip shortage … [Read more...]
Why TinyML looks like a fit for the developing world
Running small machine learning models on embedded devices has become a rich topic of research and investment, both at large tech companies and in academia. It's not really mainstream yet, but it's possible that the use cases where TinyML delivers the … [Read more...]
Privacy and new functions will make TinyML big
Privacy and smart features that don't depend on an app will likely drive the adoption of machine learning (ML) on constrained edge devices going forward. That was the message Zach Shelby, CEO of Edge Impulse, and I tried to convey when we sat on a … [Read more...]