Too Long; Didn't Read
I work at <a href="https://skylum.com" target="_blank">Skylum</a> — the company making leading AI-enabled photo editing software <a href="https://skylum.com/luminar" target="_blank">Luminar</a>, <a href="https://skylum.com/aurorahdr" target="_blank">Aurora HDR</a> and <a href="https://photolemur.com" target="_blank">Photolemur</a>. Currently, our systems use Tensorflow as a neural computation engine. Delivering optimized and small neural networks for our customers is not an easy process. There are a few things you have to keep in mind — the size of the tensorflow build itself, size of neural models and their computation speed. TF isn’t perfect for that. The size of the native TensorFlow inference engine after all optimizations is at least 60 megabytes and optimizing models of edge CPU computations isn’t perfect as well. TFLite is a depreciated solution and TF Mobile is good at what it’s doing — optimizing for mobile CPU’s. The area of pure desktop optimization isn’t covered by any major library, so it’s a part of my interest.