28 March 2024 
 
25 February 2020

Game-changing trends

The last decade has seen an amazing advancement in embedded system development techniques, tools and technologies. Today, we now have microcontrollers with clock speeds above 1 GHz with more than 4 megabytes for flash storage. This dramatic increase in capabilities for microcontrollers and their affordable costs is going to usher in a completely new design paradigm in the decade to come.

Photo: NuernbergMesse / Frank BoxlerPhoto: NuernbergMesse / Frank Boxler
Trends can be discussed amongst experts on the embedded world.
Python is already the most popular programming language used by software developers outside the embedded systems industry. There are several reasons why it might become the dominant language for embedded systems as well.

First, the compute power available in microcontrollers has grown to the point where a stripped-down version of a Python kernel can be ran on a microcontroller that costs only a few euros. Second, there are already popular open source ports for Python such as MicroPython that are available on more than a dozen architectures including popular ones like the STM32 and the ESP32. Third, C and C++ aren’t taught in most computer science or engineering programs. It’s now Python and some Java and has been for quite some time. This means that there is and will be a whole generation of engineers taking the lead in the next decade who have a natural inclination to using Python.
Machine learning at the edge
Machine learning for embedded developers, as it currently stands, has the greatest potential at the IoT edge. Up until recently, machine learning was done somewhere “out there” and it had little if anything to do with embedded developers. But the rapid advancements in hardware technologies for microcontrollers are making it far easier to run machine learning inferences on a microcontroller.

Running the inference on the embedded controller at the edge opens a whole range of local applications and can save on bandwidth and communication costs with the cloud. One area that seems particularly primed for machine learning at the edge is embedded vision. The ability to perform object detection and recognition at the edge has so many potential opportunities for business applications and for developers to lighten their workload. The vast amount of data that is available will make it easy to train new machine learning models.

https://www.embedded-world.de/

 



 

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