Microchip/On Semiconductor

Vision System: smart and energy efficient

14. Juli 2021, 15:47 Uhr | Iris Stroh

Fortsetzung des Artikels von Teil 1

ON Semiconductor and Microchip complement each other perfectly

The EVEREST-VISION-BOARD = EVEREST-DEV-BOARD + X-Celerator FMC daughterboard from ON Semiconductor
The EVEREST-VISION-BOARD = EVEREST-DEV-BOARD + X-Celerator FMC daughterboard from ON Semiconductor
© Arrow

When searching for low-power components, Arrow chose ON Semiconductor's XGS sensors and a PolarFire-(SoC)-FPGA from Microchip for its activities. The XGS sensors are global shutter sensors that feature short exposure times, which is particularly suitable for fast-moving objects. According to Schwarztrauber, the power consumption of the XGS sensors is up to 20 percent lower than competing products. Schwarztrauber sais: »ON Semiconductor is considered a pioneer in energy-efficient innovation. The XGS image sensors include high-performance, low-noise variants with 12-bit image quality at high frame rates. The new XGS 45000, XGS 32000, XGS 30000 and XGS 20000 CMOS image sensors provide up to 45 megapixel resolution images for critical applications - at up to 60 fps in 8K video mode. Further exciting new products are the XGS 5000 and XGS 16000 for low-power applications and outstanding image quality for compact camera designs measuring 29mm x 29mm. In addition to the XGS 5000 and XGS 16000, 9.4-, 8-, 3- and 2-MP variants are also available.« The PolarFire-(SoC)-FPGAs offer complexities up to 500 kLUT and can reduce power consumption up to 50 percent compared to SRAM-based FPGAs, depending on ambient temperature and system frequency.

Schwarztrauber: »Low power consumption has several advantages. If the camera systems are battery-operated, the batteries last longer. If, on the other hand, the cameras are powered by PoE, for example, the costs for the power supply are reduced. Lower power consumption also results in less waste heat in the often small and compact camera housings, which increases the image quality of the sensor data.« The Microchip/ON Semiconductor components also have the advantage of being available in small packages, also an important requirement for embedded smart vision systems.

On the basis of the PolarFire-(SoC)-FPGAs from Microchip, Arrow, together with its development partner IMG Electronic & Power Systems, has realised a development board called »EVEREST-DEV-BOARD« and demonstrated with demos that it fulfils the requirements placed on the main processing unit in a smart vision camera: »The image processing algorithms run on the FPGA and we can also ensure the necessary flexibility with regard to the interfaces to the sensor,« Schwarztrauber continues. The next step was to develop the EVEREST-VISION-BOARD, which consists of the aforementioned EVEREST-DEV-BOARD and an X-Celerator FMC daughterboard from ON Semiconductor and high-quality optics. Schwarztrauber continues: »The first prototypes are now available. A corresponding reference design is currently being developed. From August this year, the EVEREST-VISION-BOARD will be available for the Arrow TestDrive programme and for sale at Arrow.«

Vision + AI = Smart Vision

»Smart Vision is the combination of vision and AI,« Schwarztrauber continues. Implementing CNNs (convolutional neural networks) in low-power FPGAs is the right approach, he says. On one side, developers of applications such as AI cameras, drones, automotive, ML applications, but also not so common target markets such as the maker community are very interested in the AI capabilities in vision systems, on the other side, low power consumption and a small package size are typically crucial in these applications. Schwarztrauber continues: »We can use demos to show how powerful our approach is.« For example, when it comes to pattern recognition, Arrow has already demonstrated the FPGA's performance at trade fairs: With a power consumption of 0.1 to 3 W, no active cooling is necessary, lightweight systems can be realised, the FPGAs are immune to SEUs (single event upsets, unintentional flipping of bits) and »are accordingly ideally suited for AI applications in the edge area,« Schwarztrauber concludes.

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