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.

人工智能现在通过每两个月执行两倍的浮点乘法来取得进步，但半导体行业每两年在芯片上平铺两倍的乘法器。此外，越来越密集地平铺这些乘法器的回报现在越来越少，因为信号必须相对地传播得越来越远。虽然可以通过在三维芯片中堆叠平铺式乘法器来缩短行程，但这种解决方案会急剧减少用于散热的可用表面积。在这里，我建议通过从突触学习转向树突学习来超越这种三维热约束。突触输入没有精确加权，而是沿着一小段树突精心排序，称为树突中心学习。