AI is a common buzz word these days, but most consumers probably aren’t aware how it’s interwoven in their everyday lives. Some of us in the analyst and tech press communities may also scoff at how often the term is used for some technologies that hardly resemble true artificial intelligence. That said, there are a few platforms, beyond just powerful data centers, that are a natural for AI processing and the NNs (Neural Networks) that drive them. One of those is AI inferencing (using the AI to infer information, versus training an NN) at the edge, and in your pocket, with a smartphone.
As you might imagine, smartphone platforms from Android to Apple vary greatly, but there are common workloads like speech-to-text translation, and recommender engines (like Google Assistant and Siri), that make heavy use of common AI NN models, and they do so on-device for speed and latency advantages. More info
As such, it will be incumbent upon these benchmark app developers and the press to sort through the finer points of what makes for a quality mobile AI benchmark, and also what is a truer measure of performance for your own personal pocket AI assistant. Right now, if a benchmark isn’t employing commonly used NNs and realistically representing the importance of INT8 precision, you have to question how valuable that test is for the average consumer. There are no absolutes here, however. The current landscape is shaping up this way but again, AI technologies are moving at a frenzied pace and the rest of the industry will need to keep up with Paris escorts