ABSTRACT

Artificial Intelligence (AI) has progressed rapidly over the past decade, but the cost of deploying and operating AI in energy, dollars, and carbon emissions is growing unsustainably. Users currently access AI through the cloud, sacrificing personalization and privacy. This project uses inspiration from the brain, including learning with dendrites that neuroscientists have recently discovered, to reverse engineer the brain's learning rules guided by neuroscience theory and experimental techniques. These insights will be implemented in a novel neuromorphic chip using emerging three-dimensional (3-D) fabrication techniques. The success of such an approach would allow AI to run, not with megawatts in the cloud, but rather with watts on a smartphone. Thus, learning with dendrites could reign in unsustainably growing costs, distribute productivity gains equitably, personalize user experience, and restore privacy. This project also aims to increase opportunities for students through ongoing summer internship programs and new outreach efforts (Demo Day on campus for high-schoolers and Engineering Night at high schools).

Tiling computing units and stacking them in the third dimension shortens distances and cuts the energy communication uses, which now dominates the energy budget of today?s processors. But stacking reduces the surface area for dissipating heat, restricting a 3-D processor to serial, rather than parallel operation. Less heat would be produced if units communicate sparsely. This could be accomplished by exchanging patterns of binary-amplitude signals (e.g., high or low voltages on a digital bus) for sequences of unary-amplitude signals (e.g., spikes from an ensemble of neurons). But this would require synthesizing connections to precisely order synaptic inputs on a dendrite-like device, shifting the prevailing abstraction of a brain centered on learning with synapses to one centered on learning with dendrites or what the project team refers to as dendrocentric learning. To accomplish this goal, three objectives are proposed: (1) Identify how spiking sequences represent information as subsequences across cortical columns and model how a column learns to generate its subsequence. (2) Establish the combinatorial logic neighboring stretches of dendrite use to decode a sequence?s subsequences and model how a stretch of dendrite learns to detect a subsequence. (3) Emulate a stretch of dendrite?s sequence selectivity in a nanoscale electronic device, integrate it in 3-D, and design a switching network to implement the learning rules formulated in objectives 1 & 2. Achieving these objectives would enable learning in sparse environments with extreme energy efficiency leading to a transformational impact on how information technology serves society while ensuring equity and access.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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