During the Intel AI Summit earlier this month where the company demonstrated its initial processors for artificial intelligence training and inference workloads, Naveen Rao, corporate vice president and general manager of the Artificial Intelligence Products Group at Intel, spoke about the rapid pace of evolution in the AI space that also includes machine learning and deep learning. …
At this year’s Intel AI Summit, the chipmaker demonstrated its first-generation Neural Network Processors (NNP): NNP-T for training and NNP-I for inference. …
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It’s par for the course for AI chip startups to focus on peak performance on outdated benchmarks to appeal to the hardware folks who might give their gear a go for deep learning training or inference. …
If the only thing you really know to date about machine learning chip startup, Groq, is that it is led by one of the creators of Google’s TPU and that will target inference, don’t worry, you didn’t miss something. …
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. …
Startup Cerebras Systems has unveiled the world’s largest microprocessor, a waferscale chip custom-built for machine learning. …
Carey Kloss has been intimately involved with the rise of AI hardware over the last several years, most notably with his work building the first Nervana compute engine, which Intel captured and is rolling into two separate products: one chip for training, another for inference. …
As artificial neural networks for natural language processing (NLP) continue to improve, it is becoming easier and easier to chat with our computers. …
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