By Bryce Johnstone, Imagination Technologies
eeNews Europe (February 06, 2019)
One of the most talked about topics in the automotive industry today is advanced driver assistance systems (ADAS). These systems assist the driver in dealing with potential issues in a number of ways. They can provide visual and audible warnings to the driver, but they can also take control of the brakes, accelerator and steering to move the car out of the way of danger.
These systems rely on higher-quality data from an increasing number of discrete sensors, such as Light Detection and Ranging (LiDAR), which measures the distance to a target with a pulsed laser light, Radio Detection and Ranging (RADAR), which is similar to LiDAR but uses radio waves instead of a laser, and infrared (IR) cameras systems. These all enable ADAS to better interpret the environment and improve its ability to help the driver.
Introducing the GPU
Within a car’s central electronic control unit (ECU), you will find one or more large silicon devices that contain a multi-core central processing unit (CPU), a graphics processing unit (GPU), a memory subsystem that feeds the sensor data for real-time processing, and a range of other cores, such as I/O, peripheral connection, dedicated video and DSP. The GPU’s highly parallel, throughput-oriented nature makes it a great fit for the challenges of ADAS. It is essentially a turbo-charged multiply, accumulate engine which is the basis of neural network-type algorithms. Therefore, it's no wonder that many of today's leading ADAS vendors are starting to exploit the ability and performance of embedded GPUs in order to make the generational leaps in capabilities and performance required over what is available in cars today.
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