Ceva

This blog post was originally published at Ceva’s website. It is reprinted here with the permission of Ceva. Not so long ago, AI inference at the edge was a novelty easily supported by a single NPU IP accelerator embedded in the edge device. Expectations have accelerated rapidly since then. Now we want embedded AI inference

This blog post was originally published at Ceva’s website. It is reprinted here with the permission of Ceva. Packing applications into constrained on-chip memory is a familiar problem in embedded design, and is now equally important in compacting neural network AI models into a constrained storage. In some ways this problem is even more challenging

Transformers are taking the AI world by storm, as evidenced by super-intelligent chatbots and search queries, as well as image and art generators. These are also based on neural net technologies but programmed in a quite different way from more commonly understood convolution methods. Now transformers are starting to make their way to edge applications.