Microsoft to face off Apple in the AI ecosystem
Partnering with market leaders Intel, AMD, and NVIDIA, Microsoft has recently announced a new line of silicon-level improvements made specifically for AI compute at the three-day build conference. Till now Apple is the market leader in AI chips- the Apple Neural Engine (ANE). With this move, the big names of the PC ecosystem have collaborated to challenge Apple’s dominance. Microsoft is trying to compete with the M-series of chips by tying up with chipmakers to recreate the magic of ANE.
In conjunction with hardware manufacturers like Dell, HP, Lenovo, and more, this will create a new generation of personal computers with on-device AI capabilities.
AMD and Microsoft have already been reported as working together to create more capable AI chips. Reports have emerged that Microsoft is “providing support” to strengthen AMD’s AI chips. While these were only rumours before the Windows AI announcement, they now seem to be coming to fruition.
AMD’s 7040-series of laptop chips has an in-built NPU, and already works with Windows 11 through the ONNX runtime. Documentation is now available for the 7040-series chips’ AI capabilities, making it easy for developers to add AI functionality to their applications.
As part of the partnership, another company Intel has stated that its new line of Meteor Lake chips has a built-in neural vision processing unit or VPU. This will reportedly accelerate AI inference while also working in software like Adobe Premiere Pro for more “effective machine learning”. Apart from this, Intel has also added support for these chips on WinML and DirectML, so developers can directly address the new hardware.
While NVIDIA has done its part in creating the AI compute market, it seems that it’s not completely done yet. As part of the partnership with Microsoft, NVIDIA has released updates to its driver software that will make RTX-enabled GPUs even faster at AI tasks. By leveraging the GPUs’ in-built tensor cores, NVIDIA has promised performance improvements for ML models, such as a 2x improvement for Stable Diffusion.
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