How TensorFlow helps Edge Impulse make ML accessible to embedded engineers
How TensorFlow helps Edge Impulse make ML accessible to embedded engineers
“No matter where you are reading this right now—your home, your office, or sitting in a vehicle—you are likely surrounded by microcontrollers. They are the tiny, low-power computers that animate our modern world: from smart watches and kitchen appliances to industrial equipment and public transportation. Mostly hidden inside other products, microcontrollers are actually the most numerous type of computer, with more than 28 billion of them shipped in 2020.
The software that powers all these devices is written by embedded software engineers. They’re some of the most talented, detail-oriented programmers in the industry, tasked with squeezing every last drop of efficiency from tiny, inexpensive processors. A typical mid-range microcontroller—based around Arm’s popular Cortex-M4 architecture—might have a 32-bit processor running at just 64Mhz, with 256KB of RAM and 1MB of flash memory for storing a program. That doesn’t leave a lot of room for waste.
Since microcontrollers interface directly with sensors and hardware, embedded engineers are often experts in signal processing and electrical engineering—and they tend to have a lot of domain knowledge in their area of focus. One engineer might be an expert on the niche sensors used for medical applications, while another might focus on analyzing audio signals…”
Source: blog.tensorflow.org/2021/06/how-tensorflow-helps-edge-impulse-make-ml-accessible.html