Dendrocentric learning for synthetic intelligence

Kwabena Boahen ()
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Kwabena Boahen: Stanford University

Nature, 2022, vol. 612, issue 7938, 43-50

Abstract: Abstract Artificial intelligence now advances by performing twice as many floating-point multiplications every two months, but the semiconductor industry tiles twice as many multipliers on a chip every two years. Moreover, the returns from tiling these multipliers ever more densely now diminish because signals must travel relatively farther and farther. Although travel can be shortened by stacking tiled multipliers in a three-dimensional chip, such a solution acutely reduces the available surface area for dissipating heat. Here I propose to transcend this three-dimensional thermal constraint by moving away from learning with synapses to learning with dendrites. Synaptic inputs are not weighted precisely but rather ordered meticulously along a short stretch of dendrite, termed dendrocentric learning. With the help of a computational model of a dendrite and a conceptual model of a ferroelectric device that emulates it, I illustrate how dendrocentric learning artificial intelligence—or synthetic intelligence for short—could run not with megawatts in the cloud but rather with watts on a smartphone.

Date: 2022
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DOI: 10.1038/s41586-022-05340-6

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