There’s other use cases where when running inference on a CPU, there are accelerators inside that help to accelerate AI workloads directly. We estimate that 65% to 70% of inference is run today on CPUs, so it’s critical to make sure that they’re matching that hardware workload, or the hardware to the workload that you want to run, and make sure that you’re making the most energy-efficient choice in the processor.
The last area around software that we think about is carbon-aware computing or carbon-aware software, and this is a notion that you can run your workload where the grid is the least carbon-intensive. To help enable that, we’ve been partnering with the Green Software Foundation to build something called the Carbon Aware SDK, and this helps you to use the greenest energy solutions and run your workload at the greenest time, or in the greenest locations, or both. So, that’s for example, it’s choosing to run when the wind is blowing or when the sun is shining, and having tools so that you are providing the insights to these software innovators to make greener software decisions. All of these examples are ways to help reduce the carbon emissions of computing when running AI.
Laurel: That’s certainly helpful considering AI has emerged across industries and supply chains as this extremely powerful tool for large-scale business operations. So, you can see why you would need to consider all aspects of this. Could you explain though how AI is being used to improve those kind of business and manufacturing productivity investments for a large-scale enterprise like Intel?
Jen: Yeah. I think Intel is probably not alone in utilizing AI across the entirety of our enterprise. We’re almost two companies. We have a very large global manufacturing operations that is both for the Intel products, which is sort of that second business, but also a foundry for the world’s semiconductor designers to build on our solutions.
When we think of chip design, our teams use AI to do things like IP block placement. So, they are looking at grouping the logic, the different types of IP. And when you place those cells closer together, you’re not only lowering cost and the area of silicon manufacturing that lowers your embodied carbon for a chip, but it also enables a 50% to 30% decrease in the timing or the latency between the communication of those logic blocks, and that accelerates processing. That’ll lower your energy costs as well.
We also utilize AI in our chip testing. We’ve built AI models to help us to optimize what used to be thousands of tests and reducing them by up to 70%. It saves time, cost, and compute resources, which as we’ve talked about, that will also save energy.
In our manufacturing world we use AI and image processing to help us test a 100% of the wafer, detect up to 90% of the failures or more. And we’re doing this in a way that scales across our global network and it helps you to detect patterns that might become future issues. All of this work was previously done with manual methods and it was slow and less precise. So, we’re able to improve our factory output by employing AI and image processing techniques, decreasing defects, lowering the waste, and improving overall factory output.
We as well as many partners that we work with are also employing AI in sales techniques where you can train models to significantly scale your sales activity. We’re able to collect and interpret customer and ecosystem data and translate that into meaningful and actionable insights. One example is autonomous sales motions where we’re able to offer a customer or partner the access to information, and serving that up as they’re considering their next decisions through digital techniques, no human interventions needed. And this can have significant business savings and deliver business value to both Intel and our customers. So, we expect even more use at Intel, touching almost every aspect of our business through the deployment of AI technologies.
Laurel: As you mentioned, there’s lots of opportunities here for efficiencies. So, with AI and emerging technologies, we can see these efficiencies from large data centers to the edge, to where people are using this data for real-time decision making. So, how are you seeing these efficiencies actually in play?
Jen: Yeah, when I look at the many use cases from the edge, to an on-prem enterprise data center, as well as to the hyperscale cloud, you’re going to employ different techniques, right? You’ve got different constraints at the edge, both with latency, often power, and space constraints. Within an enterprise you might be limited by rack power. And the hyperscale, they’re managing a lot of workloads all at once.
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