Habana separates machine learning training from inference
By building different processors to address machine learning training and inference, Habana has been able to achieve impressive results as per its benchmarks
![georgios-anadiotis-author.jpg](https://www.zdnet.com/a/img/resize/f8a968a7322a33fc2db8533732594881a2e3bd2d/2016/06/27/83b9c200-9470-43e7-8a3e-7a824bfa8e3c/georgios-anadiotis-author.jpg?auto=webp&fit=crop&frame=1&height=192&width=192)
![traininginference.jpg](https://www.zdnet.com/a/img/resize/00a41bb966b14272704651c3f7a9226927f0f8e2/2019/01/30/3e5a254e-d8df-40d3-abff-954c25698c5f/traininginference.jpg?auto=webp&width=1280)
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Architecture for machine learning training and inference
Different machine learning workloads are better suited for different processors with different architectures, according to Habana
2 of 3 The Microprocessor report
Habana smokes inference incumbents
Habana's Goya compared to Nvidia's Tesla and other processors on inference workloads
3 of 3 Habana
Habana's Goya processor compared to CPU and GPU
Results based for the ResNet-50 inference benchmark. Note results also include power efficiency
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