Fathom delivers 150 gigaflops of performance while consuming under 1.2 watts of power. The vision processing happens locally; there's no need for devices to connect to cloud services to recognize and identify images, Dashwood said.
Fathom relies on machine-learning to crunch images, and needs to be trained to analyze pixels and provide the right context to images. That entails the creation of rich data sets against which images can be verified. That learning model is usually developed on a PC, and then transferred to work with the TensorFlow software stack on the smaller Fathom.
In most cases, there are many pixels that must be analyzed in order to get a complete understanding of an image – for example, when a person is happy, the lips take on a different structure. There's no one way to train Fathom to recognize all images, and learning models may be different for cameras, drones, robots and self-driving cars.
The creation of the rich data sets needed for image understanding involves steps like classification and labeling of pixels. Fathom uses a combination of algorithms and pixel association to understand images. In machine learning models, sentiment and face recognition capabilities have become fairly common, while distance measurement and simultaneous localization and mapping -- which involves analyzing images to update a map -- remain a challenge, Dashwood said.
Fathom also has 12 vector processors that can be programmed to do a variety of tasks. The computer also has a custom GPU subsystem that is central to vision processing.
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