[zachbee]

Neuromorphic computing basically uses individual "neurons", represented with either analog or digital circuits, which communicate using asynchronous pulses called "spikes". Unlike the human brain, neuromorphic chips are 2D, but we can replicate a good amount of neural dynamics in silicon.

It's unclear how they managed to use this to run LLMs, though. Getting GPT-2 running with SNNs is a legitimate achievement, because SNNs have traditionally lagged significantly behind conventional deep learning architectures.

https://web.stanford.edu/group/brainsinsilicon/documents/ANe... https://web.stanford.edu/group/brainsinsilicon/documents/Ben...