While the mass adoption of AI has transformed digital life seemingly overnight, regulators have fallen asleep on the job in curtailing AI data centers’ drain on energy and water resources.
I hadn’t really heard of the TPU chips until a couple weeks ago when my boss told me about how he uses USB versions for at-home ML processing of his closed network camera feeds. At first I thought he was using NVIDIA GPUs in some sort of desktop unit and just burning energy…but I looked the USB things up and they’re wildly efficient and he says they work just fine for his applications. I was impressed.
The Coral is fantastic for use cases that don’t need large models. Object recognition for security cameras (using Blue Iris or Frigate) is a common use case, but you can also do things like object tracking (track where individual objects move in a video), pose estimation, keyphrase detection, sound classification, and more.
It runs Tensorflow Lite, so you can also build your own models.
I hadn’t really heard of the TPU chips until a couple weeks ago when my boss told me about how he uses USB versions for at-home ML processing of his closed network camera feeds. At first I thought he was using NVIDIA GPUs in some sort of desktop unit and just burning energy…but I looked the USB things up and they’re wildly efficient and he says they work just fine for his applications. I was impressed.
The Coral is fantastic for use cases that don’t need large models. Object recognition for security cameras (using Blue Iris or Frigate) is a common use case, but you can also do things like object tracking (track where individual objects move in a video), pose estimation, keyphrase detection, sound classification, and more.
It runs Tensorflow Lite, so you can also build your own models.
Pretty good for a $25 device!
Yeah they’re pretty impressive for some at home stuff and they’re not even that costly.