3 ms·
To my knowledge this is mostly used by internal tools, though a number of common 3rd party apis (qr code scan) use hw acceleration under the hood. Internally th
by blululu 4y ago
To my knowledge this is mostly used by internal tools, though a number of common 3rd party apis (qr code scan) use hw acceleration under the hood. Internally there is a ton of ML running on the device. The most obvious is touch screen and inputs and the camera.
3rd party developers have acres to this via CoreML, but unless latency is critical it is usually easier to develop and run ml on the cloud. For camera apps using ml, this chip is going to be used either explicitly or implicitly.
- simonw 4y agoOh the touch screen! That's fascinating, is that definitely running stuff on the neural engine?
- blululu 4y agoIf you think about it a capacitive touch sensor provides a noisy Grayscale image and the goal is to detect and classify blobs as touch gestures as quickly and accurately as possible. Since it is running at all times and latency really burns the UX. Consequences this has always been done on a HW accelerator.
- nl 4y agoI'm pretty sure this isn't correct. Gesture recognition has been around since before the neural engine shipped and doesn't appear to be different on devices with or without it.
- GeekyBear 4y agoFor example: >Machine learning is used to help the iPad’s software distinguish between a user accidentally pressing their palm against the screen while drawing with the Apple Pencil, and an intentional press meant to provide an input. https://yugalchoubisa.medium.com/how-machine-learning-and-artificial-intelligence-usedapple-a7e61153557e https://yugalchoubisa.medium.com/how-machine-learning-and-ar...
- nl 4y agoOf course palm rejection (and many other features) are powered by ML. THis doesn't mean they run on the ANN though.