Addressing neuron-to-network energy-efficiency gap by investigating neuromorphic processors as a unified dynamical system

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This project investigates a fully-coupled, analog neuromorphic architecture where the entire learning network is designed as a unified dynamical system encoding information using short-term and long-term network dynamics. At the fundamental level, a single action potential generated by a biological neuron is not optimized for energy and consumes significantly more power than an equivalent floating-point operation in a Graphical Processing Unit (GPU) or a Tensor Processing Unit (TPU). Yet a population of coupled neurons in the human brain, using ~100 Giga coarse neural operations (or spikes) can learn and implement diverse functions compared to an application-specific deep-learning platform that typically use ~1 Peta 8-bit/16-bit floating-point operations or more. The intellectual merit of this proposal is addressing this neuron-to-network energy-efficiency gap by investigating a growth-transform neural network (GTNN) based dynamical systems framework for designing energy-efficient, real-time neuromorphic processors. First, the project is investigating how a GTNN can exploit population dynamics to improve system energy-efficiency, while optimizing a learning or task objective in real-time. Second, the project is investigating how short-term and long-term network dynamics can enable scaling the proposed GTNN to billions of neurons without the need for explicit spike-routing and by exploiting network's limit-cycle fixed-points as analog memory. Third, the project is investigating a continuous-time, analog GTNN processor that can be used to demonstrate the energy-efficiency of proposed approach compared to other benchmark neuromorphic and deep-learning processors. The project is also supporting open-source development of a GTNN simulator which will be disseminated to the neural network, neuromorphic engineering and neuroscience communities. The open-source tool will also form the basis for organizing tutorials and special sessions at IEEE conferences. The demonstration platforms developed through this project is being be used to connect with other NSF sponsored outreach programs at Washington University, which includes outreach to students belonging to underrepresented groups.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.