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From: Paul H. Christiansen3/8/2018 12:39:23 PM
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Machine Learning Inspired Semiconductor Designs



In this column/analysis, The Semiconductor Golden Era I wrote in January, I outlined how the next decade would yield more creative and innovative semiconductor designs than anything we saw in the race for performance era of the past few decades. We have enough CPU performance, and we will still see innovation in GPU design, but the overall trend is moving to efficiency around new use cases. Machine learning at the forefront.

In early February, Arm did something interesting that I have a hunch will set a new trend in silicon designs. Arm announced something called Project Trillium. One I find most interesting about Arm’s newest architecture, is how it has been designed from the ground up with machine learning in mind.

Companies like Apple, Qualcomm, and Intel have been adding tweaks to their existing architectures to support machine learning. This is because the base architectures of the designs have legacy (pre-machine learning) elements. Arm is setting the trend for new architecture designs that are built from the ground up around machine learning. Machine learning use cases will touch every aspect of our computing lives so it makes sense that all semiconductor designs going forward will heavily leverage machine learning.

Arm’s new processor includes new efficient designs for things like computer vision (object recognition) and a Neural Network for on-device training and inference. It is worth noting that both those things are being done today by computers. However, they are being done with pre-machine learning silicon architectures. Which means we could see multiples in performance in the same tasks done today with these new designs built for machine learning tasks.

The observation that today’s chips were not architected from the ground up with machine learning in mind is the point that stands out the most to me. Especially with how much machine learning is part of nearly every conversation about the future of technology and computing. This is why I’m convinced we will see everyone designing semiconductors do so with machine learning optimization and focus in mind.

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