Habana is the best kept secret in AI chips. Designed from the ground up for machine learning workloads, it promises superior performance combined with power efficiency to revolutionize everything from data centers in the cloud to autonomous cars.

As data generation and accumulation accelerates, we’ve reached a tipping point where using machine learning just works. Using machine learning to train models that find patterns in data and make predictions based on those is applied to pretty much everything today. But data and models are just one part of the story.

Another part, equally important, is compute. Machine learning consists of two phases: Training and inference. In the training phase patterns are extracted, and machine learning models that capture them are created. In the inference phase, trained models are deployed and fed with new data in order to generate results.

Both of these phases require compute power. Not just any compute in fact, as it turns out CPUs are not really geared towards the specialized type of computation required for machine learning workloads. GPUs are currently the weapon of choice when it comes to machine learning workloads, but that may be about to change.

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