Memory types for autonomous automotive applications

Overview Automotive Memory

At present, the development of fully autonomous vehicles is advancing more slowly than initially planned. As a result, expectations for autonomous driving have been scaled down. Nevertheless, the digitalization of the driver's cabin is accompanied by a growing demand for storage for the automotive industry, as cars are increasingly becoming a moving data center on wheels. That's why a profound change has taken place in the storage industry, as the automobile has become a key driver for storage technologies. This will also change the design of storage to meet the needs of the automotive industry.

At the same time, established technologies like NOR flash will play a role in supporting fast boot processes. Drivers, of course, expect the car immediately to start when they turn the key in the ignition.

Power consumption is another challenge because deploying data centers on wheels requires high computing power. It is not only limited to the processors but also to the memory. Power consumption becomes even more critical in all-electric vehicles and poses a thermal management challenge for vehicle engineers.

The enormous growth in data

In the long term, of course, autonomous vehicles will become established, especially in industrial sectors where the transition is worthwhile due to high personnel costs, such as cabs or long-distance traffic. Until that point is reached, there will be an enormous increase in the amount of vehicle data collected simultaneously and the need to store it. Furthermore, one can also assume a centralization of vehicle data processing.

The amount of data needed to be recorded is also increasing due to concepts such as Transportation as a Service (TaaS). TaaS refers to buying miles and trips without the hassle of vehicle ownership: buying and financing vehicles, maintenance, gas, insurance, and sometimes even finding and paying for storage. Using TaaS means not having to deal with the hassles of current vehicle ownership while still having access to the transportation you need.

While some autonomous vehicles require real-time communication, there are also cases where you accumulate terabytes of data over several days. One example of this is fleet management. High-capacity removable storage is becoming increasingly important in these areas.

Navigation

Like any robot that can navigate itself, the car of the future must be equipped with a whole range of sensors, algorithms and the necessary computing power. There are, for example, image and vision sensors that require computing power to process the data. These sensors provide information for navigation and other autonomous vehicle functions. Currently, most of the computing power in vehicles is geared toward working with external systems such as a GPS satellite for navigation and the internal navigation system via sensors. A combined algorithm processes the data very quickly and responds very accurately. However, there is always the possibility that vehicles will be cut off from external guidance systems due to severe weather and other causes. In that case, the cars must continue to move on their own using their internal system. Therefore, these systems need to run independently and use their computing power and memory.

Tools for development

Protocol analyzers, with which the data traffic can be streamed and displayed decrypted, as well as programmers with the support of a large number of memory ICs are suitable for designing, testing and debugging memory designs.