There is much at stake in the world of datacenter inference and while the market has not yet decided its winners, there are finally some new metrics in the bucket to aid decision-making. …
It would be convenient for everyone – chip makers and those who are running machine learning workloads – if training and inference could be done on the same device. …
Processor hardware for machine learning is in their early stages but it already taking different paths. …
There are two trends converging in AI inference and so far, only a small number of companies are enmeshed. …
In the deep learning inferencing game, there are plenty of chipmakers, large and small, developing custom-built ASICs aimed at this application set. …
There are an increasing number of ways to do machine learning inference in the datacenter, but one of the increasingly popular means of running inference workloads is the combination of traditional CPUs acting as a host for FPGAs that run the bulk of the inferring. …
There is a battle heating up in the datacenter, and there are tens of billions of dollars at stake as chip makers chase the burgeoning market for engines that do machine learning inference. …
This week we have heard much about the inference side of the deep learning workload, with a range of startups emerging at the AI Hardware Summit. …
Gentlemen (and women), start your inference engines.
One of the world’s largest buyers of systems is entering evaluation mode for deep learning accelerators to speed services based on trained models. …
Over the last several years we have seen many new hardware architectures emerge for deep learning training but this year, inference will have its turn in the spotlight. …
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