Johns Hopkins electrical and computer engineers are pioneering a new approach to creating neural network chips—neuromorphic accelerators that could power energy-efficient, real-time machine intelligence for next-generation embodied systems like autonomous vehicles and robots.
Electrical and computer engineering graduate student Michael Tomlinson and undergraduate Joe Li—both members of the Andreou Lab—used natural language prompts and ChatGPT4 to produce detailed instructions to build a spiking neural network chip: one that operates much like the human brain.
Through step-by-step prompts to ChatGPT4, starting with mimicking a single biological neuron and then linking more to form a network, they generated a full chip design that could be fabricated.
“This is the first A...
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