Often, companies have their devices designed by electrical engineers. This is a cost-effective option. Custom boards do not have extra components, such as unnecessary Bluetooth or extra USB ports. However, predicting CPU and RAM requirements of an ML model at board design time is difficult. Starter kits can be useful tools to use until the hardware requirements are understood. The following boards are among the most widely adopted boards on the market:
- Manifold 2-C with NVIDIA TX2
- The i.MX series
- LattePanda
- Raspberry Pi Class
- Arduino
- ESP8266
They are often used as a scale of functionality. A Raspberry Pi Class device, for example, would struggle with custom vision applications but would do great for audio or general ML applications. One determining factor for many data scientists is the programming language. The ESP8266 and Arduino need to be programmed in a low-level language such as C or C++, while devices such as Raspberry Pi Class or above can be programmed...
