@Nona_xai: Google DeepMind chip engineer Reiner Pope just explained on a whiteboard what no one had ever explained to you before: …

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Google DeepMind chip engineer Reiner Pope delivers a comprehensive whiteboard explanation of how chips work, covering logic gates to systolic arrays and the human brain, in a free YouTube video.

Google DeepMind chip engineer Reiner Pope just explained on a whiteboard what no one had ever explained to you before: how a chip really works, from the logic gate to the human brain. In 1h15 he covers everything that engineering schools take years to teach. → How to build multiplication from scratch. → Why moving data costs more than computing. → How systolic arrays work that make LLMs run. → Why a CPU core is 10 times larger than a GPU one. → Why a GPU is nothing more than a bunch of small TPUs. → And why the human brain remains an enigma for hardware architects. It's free. It's on YouTube. And it's the best hour you'll spend this week if you want to understand why hardware is the true battleground of AI.
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Google DeepMind chip engineer Reiner Pope just explained on a whiteboard what no one had ever explained to you before: how a chip really works, from the logic gate to the human brain.

In 1h15 he covers everything that engineering schools take years to teach.

→ How to build multiplication from scratch. → Why moving data costs more than computing. → How systolic arrays work that make LLMs run. → Why a CPU core is 10 times larger than a GPU one. → Why a GPU is nothing more than a bunch of small TPUs. → And why the human brain remains an enigma for hardware architects.

It’s free. It’s on YouTube. And it’s the best hour you’ll spend this week if you want to understand why hardware is the true battleground of AI.

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@bookwormengr: Interview with Richard Ho (leader of OpenAI's Jalapeno chip).

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In the interview, OpenAI VP of Hardware Richard Ho looks back on his chip career — from Stanford and the D. E. Shaw Anton supercomputer to eight generations of Google TPUs — and reveals the team-building philosophy behind OpenAI's in-house accelerator, Jalapeno: team first, ideas second; a blank-sheet design approach; and, most importantly, the key engineering lesson of keeping hardware programmable and avoiding overfitting to specific models.