The selection and use of FPGAs for automotive interfacing, security,
By Clive "Max" Maxfield Contributed By DigiKey's North American Editors
Traditionally, computation tasks in automobiles have been performed by microcontroller units (MCUs) and application processors (APs). A typical mid- range vehicle can contain 25 to 35 MCUs/APs, while luxury cars may employ 70 or more. Increasingly, automobiles require extremely sophisticated, computationally intensive capabilities for such tasks as advanced driver assistance systems (ADAS), infotainment, control, networking, and security. Many of these applications involve machine vision in the form of image and video processing coupled with artificial intelligence (AI). Alone, the processor architecture struggles to handle all of the electrical interfaces and protocols that are demanded by peripheral devices like sensors, cameras, and displays. Also, in many cases, these processors simply cannot satisfy the extreme computational demands of tasks like machine vision and AI. To address this complexity, designers of automotive systems are turning to field-programmable gate arrays (FPGAs), not to replace the existing MCUs/APs, but rather to act as bridges between them and other devices, and to augment them by offloading communications and other computationally intensive tasks.
Since FPGAs can be programmed to support a wide variety of electrical interfaces and protocols, they can act as bridges between MCUs/APs and sensors, cameras, and displays. Also, because they can perform calculations and operations in a massively parallel fashion, FPGAs can be used to execute computationally intensive vision processing and AI tasks, thereby freeing up the host processors for other activities. This article discusses the processing requirements of modern vehicles and describes some of the automotive applications that can be addressed by FPGAs. It then introduces some example FPGAs from Lattice Semiconductor and shows how they can be used to solve connectivity, processing, and security problems. Associated development boards are also presented to help designers get started.
and compute- intensive loads
Target automotive applications for FPGAs
To support their ADAS capabilities, today’s automobiles employ many sensors outside the vehicle, including cameras, radar, LiDAR, and ultrasonic detectors. In many cases, it is necessary to take data from disparate sensors, pre- process this data (removing noise and formatting it as required),
we get technical
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