Al-Silicon: On The Nose Or A Nose Job?

The new AI wars in technology industry are happening on wafer-thin ice, oops, silicon. Here’s why and how savagely!

-  Pratima H

Everything except the kitchen sink. That seems to be the mood in Big Tech chateau as the players pack and move from their penthouses towards the new and emerging AI estate. Almost every big name now has a pony in the custom-silicon ring, playing a bigger and deeper AI game. If you thought these are showponies, you should brace up for the new workhorserace that's brewing a fierce dust-storm.

AI: WITH AN EXTRA SIDE OF CHIPS

At AWS re:Invent, AWS announced the next generation of its two AWS-designed chip familiesAWS Graviton4 and AWS Trainium2-promising better price performance and energy efficiency for customer workloads, in Machine Learning (ML) training and Generative Artificial Intelligence applications. Note that Trainium2 was described here as something designed to deliver up to 4x faster training than first generation Trainium chips - something strong enough to train foundation models (FMs) and large language models (LLMs) in a fraction of the time, while improving energy efficiency up to 2x.

Then there is Google, with its fifth-generation tensor processing unit (TPU) and TPU v5e- which it said is designed to train large models. Not to forget, the big patent lawsuit over TPU designs which happening on the sidelines. And there is Microsoft that unboxed its two custom-designed chips and integrated systems. It began this path with the Microsoft Azure Maia AI Accelerator, optimised for AI tasks and generative AI. It also has the Microsoft Azure Cobalt CPU, an Arm-based processor tailored to run general purpose compute workloads on the Microsoft Cloud. Well, if these three are here, no wonder, more will follow suit.