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Hidden Costs In Faster, Low-Power AI Systems

By: Ed Sperling

Tradeoffs in AI/ML designs can affect everything from aging to reliability, but not always in predictable ways.

Others agree. “Reconfigurable hardware platforms allow the needed flexibility and customization for upgrading and differentiation without requiring rebuilding,” said Raik Brinkmann, CEO of OneSpin Solutions. “Heterogenous computing environments that include software programmable engines, accelerators, and programmable logic are essential for achieving platform reconfigurability as well as meeting low latency, low power, high-performance and capacity demands. These complex systems are expensive to develop so anything that can be done to extend the life of the hardware while still maintaining customization will be essential.”


What isn’t clear, though, is how those systems work in conjunction with other systems, what the impact of various power-saving approaches will be, and how these systems ultimately will interface with other systems when there is no human in the middle. In some cases, accuracy has been been unexpectedly improved, while in others the results are muddy, at best. But there is no turning back, and the industry will have to begin sharing data and results to understand the benefits and limitations of installing AI everywhere. This is a whole different approach to computing, and it will require an equally different way for companies to interact in order to push this technology forward without some major stumbles.


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