The push for ultra-low power and low-latency deep learning models, computing hardware, and systems for inference on edge devices continues to create exciting new opportunities for AI in daily life. However, designing such systems can introduce significant overhead in terms of trial & error, domain expertise and engineering challenges, particularly when trying to preserve model accuracy after extreme model compression. In this talk, we introduce a novel, automated method for compressing convolutional neural networks to preserve maximum accuracy on the target application by utilizing a “network within network” paradigm. In this approach we approximate the individual layers within a neural network using smaller neural networks to find highly accurate, highly compact CNN topologies that satisfy strict constraints on model size and acceptable accuracy. We will discuss some results, the implications of this approach for tinyML applications and future developments in the field.

Imagimob is a pioneer in Edge AI (=TinyML) with experience from 20+ Edge AI customer projects. Customers include Scania, Husqvarna and Flir Systems. The first commercial product using an Imagimob Edge AI application was commercially launched in 2018. This presentation is a case study where we are demonstrating how we developed gesture-controlled headphones using a radar sensor (Acconeer) and Edge AI applications, a concept that was presented at CES 2020 in Las Vegas. The presentation will demonstrate how Imagimob AI (SaaS) is used in the development of the application.