A TensorFlow Lite Micro Speech model that detects wake words and turns on a different coloured LED light to emulate traffic lights.
Machine learning typically involves lots of computing power, and these are usually in the form of a large data center with GPUs and the costs of training a deep neural network can be astronomical. The emergence of tiny neural networks, which are as small as 14 KB, opens a plethora of doors to new applications that can analyze data right on the microprocessor itself and derive actionable insights (Warden and Situnayake, 2019). This saves time and prevents latency because we do not have to transmit data to a cloud data center for it to be processed and wait for it to come back (Warden and Situnayake, 2019). Such a phenomenon is called Edge Computing and allows for data to be processed and computed on the device that it is stored (Lea, 2020).