CFU Playground (3) - ML on a board (Arduino nano 33 ble )
I am going through this step without running Tensorflow lite micro in the Risc-V environment in the CFU Playground. It doesn't matter if you are familiar with Tensorflow lite micro, but if not like me, you need to port your own model and check if it works properly. The reason for choosing Arduino nano 33 ble is that it has luxurious specifications compared to Arduino uno. It has Cortex-M4, 64Mhz, 256KB SRAM, 1MB Flash Memory, and sensors such as a gyroscope built-in. So, you can do interesting experiments in the future. Starting with a board with too little memory and poor CPU performance can incur many complicated errors. And you have to spend a lot of effort to optimize your code for fitting into the memory. https://store-usa.arduino.cc/products/arduino-nano-33-ble?selectedStore=us When it comes to price, ESP32 DevKit can also be an alternative. But Tensorflow Lite Micro in ESP32 is supported only on ESP-IDE. The ESP-IDE is less convient than Arduino IDE. Above all, the advantage...