Making the artificial intelligence of things a reality: Page 3 of 4

October 21, 2019 //By Mark Lippett
artificial intelligence
The advent of the Internet of Things means that machines that have always existed in isolation are suddenly capable of generating data and ‘talking’ to one another in ways that open up revolutionary modes of operation for a huge variety of applications.

The hardware behind it

As such, there’s a rapidly growing demand for chips that can enable high performance processing without a debilitating price tag. Any manufacturer looking to produce a processor that meets the needs of these businesses has a precarious balancing act to perform:

  1. Cost: The processors that meet this need to be cheaper than the premium alternatives that many would assume were the only solution to the need for compute power. There’s a wealth of trip hazards to negotiate here, from the expenses of third-party hardware/software to the category of the processor and the components it requires to function.

  2. Performance: Cheap cannot mean compromising on performance, or the entire operation becomes pointless. The minds behind the product need to be able to compensate for cost-cutting measures with sophisticated algorithms and architectures to ensure that the quality bar is met.

  3. Versatility: Perfecting the split between price and performance is all well and good, but if the result is a chip purpose-built for one use – say, driving voice recognition – then the majority of the AIoT industry hasn’t been served a solution at all. You see, the big secret is that AIoT is not really a market at all, it is the sum of 100 markets, and 10,000 market segments – each with different needs. The endpoint solution must flexibly and affordably deliver the combination of compute classes (AI, DSP, control and IO) required by each of these segments.


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