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UI as the next battlefield in CE (Part 2)

Posted: 05 Apr 2012 ?? ?Print Version ?Bookmark and Share

Keywords:predictive intelligence? cloud computing? general-purpose processor?

In part 1, we tackled how the next battle in consumer electronics is shifting to user interface (UI) technology. Rather than compare minor differences in features sets, consumers will increasingly make purchases based on ease of use and access of features. System designers face increasing pressure to produce UIs that work flawlessly in order to catch the next product cycle. User Interface innovation will eventually shift to speech recognition and other more complex UIs. As this shift occurs, consumers will be faced with adopting many new and exciting technologies over the next several years. In the next phase of UI adoption, rather than require user training, devices with advanced UIs will need to adapt to consumers so they can be used "out of the box". To achieve this, devices need to become more intelligent and, as such, will require more processing power and memory.

With regards to system design, adding intelligence to a system increases the need for additional processing capacity and memory, not just to diagnose the system or make a prediction but also to implement the complex responses the system needs to take. The complexity of predictive intelligence requires more processing and memory resources than what is currently offered in embedded systems. As these embedded systems take on more UI functionality, this issue is becoming more of a challenge, both on the processing and memory side of the equation, given the resource-limited nature of embedded systems. The addition of more processing capabilities is specially challenging in embedded systems as higher performance translates into systems demanding more power. This is a constraint that directly relates to battery life or expensive design constrains for adequate power dissipation which ultimately influences user experience.

Using the cloud
In recent years, cloud computing has become a popular trend across the technology universe. As more applications are moved to the cloud, one key challenge for OEMs is determining where to implement intelligent processing. With near-ubiquitous connectivity, whether high speed Ethernet or 3G/4G cellular networks, the electronics industry is blurring the lines between onboard functionality and cloud-based features. For many applications, cloud computing is a highly attractive option for a number of reasons, particularly for embedded devices. One key driver is total cost of ownership: rather than increasing equipment cost by integrating additional computing and storage resources on each device, processing and memory are centralized using the network. This approach allows companies to leverage their technology investment across multiple applications. However, in certain applications this choice comes with a trade off, as companies balance their priorities between cost and performance.

In the automotive infotainment segment, cloud computing is used partially to enable advanced functionality for speech recognition, but this feature usually carries a performance penalty. The advantage of the cloud-based approach is it provides greater accuracy (more advanced analysis techniques and features such as agent assist). If a particular phrase cannot be recognized due to an accent or external noise, it can be routed to an operator for further evaluation. For applications such as Natural Language Understanding (NLU), which require substantially higher resources to implement, the cloud can be used to calculate various complex algorithms in order to increase accuracy. However, the performance penalty with using the cloud is primarily related to latency. Given the cloud-based approach relies upon network connectivity (in the case of an automobile, a mobile phone), the cellular connection could become the performance bottleneck, due to the inherent nature of cellular coverage. For example, if the cellphone has a weak connection or if the car or user lack a cellphone modem completely, many of the vehicle's basic UI functions may be negatively impacted, or deemed completely worthless. In addition, the IP networks over which data travels can drop packets, resulting in unreliable responsiveness. As such, this approach often results in noticeable latency being added to the user interface response. With respect to the UI, the result is an inconsistent outcome with varying latency, accuracy, and availability.

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