* The weights are currently around 6MiB uncompressed, but most of the networks can be sparsified to some extent, so that could be reduced somewhat. I also have a very fast sparse inference engine, but that is currently not in use, as the main win is on CPU, whereas I am mostly using GPUs for the NNs at the moment as it draws less power.
* I did not measure it methodically, but am always careful to not overheat the device when testing (XCode allows you to track this). My main testing device is an iPhone 11, and battery drain does not seem to be an issue compared with e.g. Zoom or Facetime. Where H265 currently wins is when you want to run in higher resolutions, but H265 is not available everywhere, say on slightly older iPads and there is no license included on Windows unless the customer pays separately.
* A WebGPU port would be nice, but I am currently waiting for the APIs to stabilize. If I can find some funding this will be a priority.
* I am not using CoreML but writing my own Metal compute shaders, but am using the "neural" parts of the Apple GPU through other APIs (MPS). I also have support for D3D and OpenCL, but have only tested the latter on teensy Mali GPUs, which at the time did not show impressive performance. On Android my approach would be to target Vulkan now that OpenCL is deprecated, I believe I have most of the plumbing in place, and speculate that things would work on modern mid-to-high end devices.
* When not cutting code, I am working on a plan for enterprise markets. Personally I have found the MacOS version really useful for pair-programming style scenarios, so that could be what I will be going after.
(The reason the MacOS version is still only in beta is because I hit a bug in AVFoundation where capturing from the web camera seems to be burning lots of CPU for absolutely no reason, and I don't want people to come away with the impression that it is my app doing that.)
* I did not measure it methodically, but am always careful to not overheat the device when testing (XCode allows you to track this). My main testing device is an iPhone 11, and battery drain does not seem to be an issue compared with e.g. Zoom or Facetime. Where H265 currently wins is when you want to run in higher resolutions, but H265 is not available everywhere, say on slightly older iPads and there is no license included on Windows unless the customer pays separately.
* A WebGPU port would be nice, but I am currently waiting for the APIs to stabilize. If I can find some funding this will be a priority.
* I am not using CoreML but writing my own Metal compute shaders, but am using the "neural" parts of the Apple GPU through other APIs (MPS). I also have support for D3D and OpenCL, but have only tested the latter on teensy Mali GPUs, which at the time did not show impressive performance. On Android my approach would be to target Vulkan now that OpenCL is deprecated, I believe I have most of the plumbing in place, and speculate that things would work on modern mid-to-high end devices.
* When not cutting code, I am working on a plan for enterprise markets. Personally I have found the MacOS version really useful for pair-programming style scenarios, so that could be what I will be going after.
(The reason the MacOS version is still only in beta is because I hit a bug in AVFoundation where capturing from the web camera seems to be burning lots of CPU for absolutely no reason, and I don't want people to come away with the impression that it is my app doing that.)