By Geoff Tate, Flex Logix
EETimes - January 29, 2019
Advice on how to compare inferencing alternatives and the characteristics of an optimal inferencing engine.
In the last six months, we’ve seen an influx of specialized processors to handle neural inferencing in AI applications at the edge and in the data center. Customers have been racing to evaluate these neural inferencing options, only to find out that it’s extremely confusing and no one really knows how to measure them. Some vendors talk about TOPS and TOPS/Watt without specifying models, batch sizes or process/voltage/temperature conditions. Others use the ResNet-50 benchmark, which is a much simpler model than most people need so its value in evaluating inference options is questionable.
As a result, as we head into 2019, most companies don’t know how to compare inferencing alternatives. Many don’t even know what the characteristics of an optimal inferencing engine are. This article will address both those points.
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