THE VIDEO SHELF · deep learning hardware
HC2023-K2: Hardware for Deep Learning
Bill Dally of NVIDIA gives a Hot Chips 2023 keynote on the challenges of building hardware for deep learning. He traces how GPUs have been adapted for machine learning, especially training.
Why HN readers recommended it
HN points to it for an insider view of why NVIDIA's AI gains came from design rather than process, and for data-movement power costs.
This is a summary from the source dataset, not an endorsement by Hacker News or the video creator.
Video & recommendation details
- Creator / channel
- hotchipsvideos
- Topic
- deep learning hardware
- Format
- talk
- Level
- advanced
- HN recommenders in dataset
- 2
- HN mentions in dataset
- 3
- Recommendation years in dataset
- 2023–2025
These counts and years describe the included Hacker News records; they are not YouTube views or the video’s publication date.
From the original HN conversations
Excerpts from the recorded comments. Follow each source for the full comment and surrounding discussion.
Neuromorphic computation has been hyped up for ~ 20 year by now. So far it has dramatically underperformed, at least vis-a-vis the hype. The article does not distinguish between training and inference. Google Edge TPUs [LINK] each one is capable of performing 4 trillion operations …
Footpost on Hacker News · 2025-06
In Neuromorphic computingI think your biggest mistake is ignoring the amount of software that runs on top of the hardware. There were times in the past where others had a higher number of FLOPS, but it didn’t make a dent in the usage of CUDA. My standard …
tverbeure on Hacker News · 2023-10
In Nvidia, one of tech's hottest companies, is fine with remote work
Summaries, classifications and recommendation records are from the HackerTube source dataset. Read about this library and its limitations.
