EMBEDDED VISION
Board-level beauty
Greg Blackman examines the effort that goes into developing embedded vision systems
A
nnouncements last year of Nvidia’s plan to acquire Arm and AMD to buy Xilinx will inevitably impact
embedded vision developers. In an opinion piece reacting to the news, Jonathan Hou, president of Pleora Technologies, said that vision developers will benefit from a unified programming interface from each vendor, but that developers will have to make a choice – more so than before – to develop on a specific platform. What the next generation of computer
chips from the combined forces of Nvidia and Arm, and AMD and Xilinx will look like remains to be seen. But those building embedded vision products today have a reasonably broad choice of embedded boards, as well as MIPI camera modules, according to Jan-Erik Schmitt, VP of sales at Vision Components. He said that how easy or difficult it is to develop an embedded vision system depends on the knowledge of the engineer, but that compared to 25 years ago – which is how long Vision Components has been making embedded vision components and systems – it is now a lot easier. Most systems are based on Linux, which a lot of computer vision programmers are familiar with and for which there is a good choice of CPUs, mainly Arm-based. Schmitt sees interest in embedded vision in the medical field, for medical analytical devices such as DNA sequencing and blood analysis. Here, it’s usually a switch from PC systems using USB cameras to embedded
processing boards, to get rid of the PC. Tere are also a lot of new tech applications, Schmitt noted, often based on AI – devices for smart cities and smart appliances, for example. AI is also finding uses in medical devices.
Te rise of MIPI CSI-2 To build an embedded vision system involves connecting a camera to a processing board, for which the MIPI CSI-2 interface is becoming more important, Schmitt said. MIPI CSI-2 has its origins in the consumer sector, where it is used in mobile phones, tablets and cameras internal to PCs or laptops. It is a low power solution, which is important for edge devices and embedded platforms, where power consumption has to be kept low. It has started showing up in industrial or semi-industrial applications, as companies like Qualcomm or Nvidia expand into sectors outside consumer, bringing the MIPI interface with them on their boards. Areas like automotive with autonomous driving, and IoT, are thought to be the next big markets for computer chips. Te data rate of MIPI CSI-2 is around
1.5Gb/s per lane, and depending on the system and sensors there can typically be up to four lanes, giving around 6Gb/s bandwidth. Te cable length from the image sensor to the processing platform is limited to about 200mm for robust data transmission, which is fine for most embedded applications. ‘Te MIPI interface is a good one, but the
Vision Components MIPI modules can be connected to various embedded processors, including Nvidia Jetson boards
12 IMAGING AND MACHINE VISION EUROPE FEBRUARY/MARCH 2021
percentage of standardisation is quite low,’ Schmitt said. ‘Tis has many advantages, but in terms of speed of development it can be a disadvantage.’ One thing that Schmitt says embedded vision developers need to be aware of is that
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Vision Components
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