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

该项目研究了一种完全耦合的模拟神经形态架构，其中整个学习网络被设计为使用短期和长期网络动态编码信息的统一动态系统。在基本水平上，由生物神经元生成的单个动作电位没有针对能量进行优化，并且比图形处理单元（GPU）或张量处理单元（TPU）中的等效浮点运算消耗更多的功率。然而，与通常使用~1 Peta 8位/16位浮点运算或更多的特定于应用的深度学习平台相比，使用~100 Giga粗神经运算（或尖峰）的人脑中的耦合神经元群体可以学习和实现各种功能。这个建议的智力价值是解决这个神经元到网络的能源效率的差距，通过调查的增长-转换神经网络（GTNN）为基础的动态系统框架设计节能，实时神经形态处理器。首先，该项目正在研究GTNN如何利用种群动态来提高系统能效，同时实时优化学习或任务目标。其次，该项目正在研究短期和长期网络动态如何能够将拟议的GTNN扩展到数十亿个神经元，而无需明确的尖峰路由，并利用网络的极限环固定点作为模拟存储器。第三，该项目正在研究一种连续时间模拟GTNN处理器，可用于证明与其他基准神经形态和深度学习处理器相比，所提出方法的能效。该项目还支持GTNN模拟器的开源开发，该模拟器将分发给神经网络，神经形态工程和神经科学界。该开源工具还将成为在IEEE会议上组织教程和特别会议的基础。通过该项目开发的示范平台被用于与其他NSF赞助的华盛顿大学的外展计划联系，其中包括对属于代表性不足群体的学生的外展。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

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.