User Interface - The Next Battlefield for Infotainment Systems

The next battleground for infotainment systems will turn to user interface (UI) technology. Consumers are no longer making small changes in feature sets when making purchasing decisions, but are increasingly considering ease of use and features. In order to catch up with the next generation of product cycles, system designers are facing increasing pressure to develop UIs for flawless work. The development of user interface technology will eventually move toward speech recognition and other more complex UIs. As this trend evolves, consumers will have the opportunity to experience many new and exciting innovations in the next few years. In the next stage of UI technology popularization, user training is no longer required, but equipment using advanced UI needs to adapt to the needs of consumers and can be used “out of the box”. In order to achieve this goal, device requirements become more intelligent, requiring more processing power and more memory resources.

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In terms of system design, adding system intelligence will increase the need for additional processing power and memory, because not only to diagnose the system or make predictions, but also to achieve the complex response that the system needs to make. Predicting the complexity of intelligence requires much more processing power and memory resources than current embedded systems can provide. As these embedded systems carry more UI functionality, this problem is becoming more and more challenging, and given the limited resource characteristics of embedded systems, processing power and memory resources are even more stretched. Adding more processing power is more challenging for embedded systems because higher performance means that the system needs more power. This is a constraint directly related to battery life and an expensive design constraint associated with adequate power consumption that will ultimately affect the user experience.

Use the cloud

In recent years, cloud computing has become a popular trend in the technical field. As more applications move to cloud processing, an important challenge for OEMs is to determine where to implement smart processing. The boundaries between onboard and cloud-based features in the electronics industry are becoming increasingly blurred with virtually ubiquitous connectivity, including high-speed Ethernet or 3G/4G cellular networks. For many applications, cloud computing is attractive for a number of reasons, especially for embedded devices. A key driver is the total cost of ownership: there is no need to integrate additional computing and storage resources on each device to increase equipment costs, but rather centralized processing and memory across the network. This approach allows companies to leverage their technology investments in multiple applications. However, this choice can also lead to trade-offs in some applications because companies need to trade off between cost and performance.

In the automotive infotainment field, the cloud computing component is used to support advanced speech recognition capabilities, but this feature typically has an impact on performance. The advantage of cloud computing technology is that it provides higher accuracy (more advanced analysis techniques and features, such as proxy assistance). If a particular statement cannot be identified due to accents or external noise, it can be forwarded to the operator for further evaluation. For applications such as Natural Language Understanding (NLU) that require more resources to implement, various complex algorithms of cloud computing can be used to improve accuracy. However, the impact of using the cloud on performance is primarily related to latency.

Since cloud technology needs to rely on network connectivity (mobile phones in automotive applications), cellular connectivity will be a performance bottleneck due to inherent cellular coverage. For example, if the cellular phone's connection signal is weak, or if the car or user does not have a cellular phone modem at all, the basic UI functionality of many vehicles may be adversely affected or may seem completely worthless. In addition, IP networks that transmit data may experience packet loss, resulting in unreliable responses. Again, this approach often results in a noticeable delay in the user interface response. For the UI, it will lead to inconsistent results in terms of latency, accuracy and usability.

The challenge for infotainment system OEMs is to strike a balance between the two needs, one that supports "vehicle" applications that primarily use embedded resources in each in-vehicle device; another requirement is driver and car The interaction of the outside world. On the one hand, there are many functions that are always important for the handling and safety of the vehicle and the driver, such as volume control, multimedia functions or mobile phone access. On the other hand, consumers have always maintained high demands on access to maps or social network resources. As a result, many OEMs have adopted a hybrid approach that focuses on onboard processing resources for critical in-vehicle functions and uses cloud computing technology for less-used and advanced features as needed. This approach allows OEMs to introduce systems that are both high performance and affordable.

UI processor

Embedded systems are increasingly demanding high-performance processing power. This is even more prominent in the automotive infotainment arena, and competition among OEMs is driving innovation and promotion of advanced computing capabilities to support multi-core architecture and 3D graphics capabilities. At the same time, many other features in the automotive environment continue to compete for available computing resources. This burden is mainly concentrated on the application processor as the main computing engine, and many functions such as 3D graphics and advanced communication protocols further burden the application processor. Therefore, these embedded systems have very limited resources available for other functions.

The user interface is a good example of the inherent nature of limited infotainment resources and their impact on key functions. For example, advanced speech recognition requires both high MIPS and fast memory access. While system designers have been working hard to develop highly optimized infotainment systems that enable them to support a variety of advanced features and state-of-the-art UIs, they are still limited by available technology, especially hardware that meets design requirements. The UI is increasingly requiring access to advanced lookup tables and algorithms, resulting in direct competition with other system functions. Ultimately, the requirement to perform multiple recognition algorithms on a general purpose processor still affects the low power requirements of these systems. Even though advanced application processors are rapidly shifting from single-core technology to dual-core and quad-core technologies for high-end infotainment systems, we believe that the UI will still be a compute-intensive feature requiring independent hardware accelerators and flexible software algorithms.

As infotainment system OEMs continue to integrate advanced human machine interface (HMI) features such as touch and speech recognition, UI functionality will remain a key system bottleneck. Between the UI bottlenecks and design challenges that OEMs must address, they also need to be able to handle complex software algorithms; be able to work accurately in a variety of noisy environments, such as in tight cabins; and be able to support data security and access Content privacy requirements; protects devices from malicious attacks and supports real-time off-board interaction with location services and ads. As more forms of UI are introduced into these systems, next-generation systems will face additional challenges in implementing multiple forms of recognition in the same system—language, voice, image, facial features (see Figure 1). The real-time requirements of a single UI technology continue to affect the reliable operation of the application processor, and the introduction of additional UI will only exacerbate this problem as these new features will further compete for shared resources.

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Figure 1: A single-function IC will rapidly evolve into a dedicated UI processor to implement and offload multiple forms of recognition processing from the application processor.

In the future, in order to achieve the performance, accuracy, and power levels required for advanced UI processing, the system will require a separate "UI processor" that is essentially used as a coprocessor. The device will integrate dedicated hardware accelerators that perform basic and advanced UI functions much faster than general purpose processors. In addition, these processors will integrate enough memory resources and an arithmetic logic unit (ALU) dedicated to improving the performance of lookup tables. We believe these UI processors are extremely efficient, can handle advanced UI functions faster, have shorter latency, and consume less power than traditional implementations that use general purpose application processors. Other potential applications for UI processors include interaction with users, making the UI easier to operate, and providing a system sleep mode to save power. While current system designers must choose between balanced accuracy and latency, dedicated UI processors allow developers to further optimize their systems to make their work more efficient.

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