Reconfigurable Neuromorphic Computing to enable Energy-Efficient Edge Intelligence

该项目的目标是通过从人类大脑中汲取灵感，开发一种节能和智能的极端边缘设备。极端边缘设备可以实现各种应用。具体来说，这些设备可用于远程跟踪北极的快速变化，用于深空探测的空中和地面机器人的自主导航，以及监控和保护关键基础设施。然而，目前的技术需要在云中处理遥感数据。这种框架有几个缺点，例如感测和决策之间的延迟，由于高功耗导致的电池寿命较短，以及与数据传输相关的隐私问题。作为该提案的一部分开发的硬件将寻求解决之前提到的挑战，并将推进极端边缘设备的最新技术。具体来说，这笔赠款将用于开发一种可重新配置的大脑启发处理器，该处理器具有根据输入数据进行学习的能力。 作为该提案的一部分开发的硬件将用于培训和激励微电子领域的下一代学生。此外，开发的硬件将使用开放源码计算机辅助设计工具，以便更广泛地传播开发的技术。 该研究提出了开发一种可重构的混合信号神经形态硬件，用于处理极端边缘的数据。该提案旨在通过采用混合信号电路对神经元和突触进行建模来提高神经形态硬件的能量效率。此外，我们建议将可编程混合信号电路拓扑结构，并探索技术，以共同优化的硬件和软件模型，学习和适应网络参数的失配和变化的存在。此外，该提案还将探索电路拓扑结构，以执行片上学习。基于这些单独的元素，该提案将研究一种系统架构，以开发可重新配置的硬件，该硬件可以编译具有片上学习能力的尖峰神经网络，以执行极端边缘任务。我们计划验证开发的硬件的极端边缘任务是使用公开可用的数据集和电池容量有限的四轴飞行器执行对象检测的对象检测。该提案将解决在设计具有变化和失配的混合信号尖峰神经网络的学习算法、用于以有限资源执行片上学习的电路拓扑和系统架构方面的知识差距，并推进能源领域的最新技术该奖项反映了NSF的法定使命，并通过使用基金会的智力价值进行评估而被认为值得支持和更广泛的影响审查标准。

The goal of this project is to develop an energy-efficient and smart extreme edge device by taking inspiration from the human brain. Extreme edge devices can enable a variety of applications. Specifically, the devices can be used for remote tracking of rapid changes in the arctic, autonomous navigation of aerial and ground robots for deep space exploration, and monitoring and securing critical infrastructure. However, the current technology requires remotely sensed data to be processed in the cloud. This framework has several drawbacks, such as the delay between sensing and decision, shorter battery life due to high power consumption, and privacy concerns related to data transfer. The hardware developed as part of this proposal will seek to address the previously noted challenges and will advance the state-of-the-art in extreme edge devices. Specifically, the grant will enable the development of a reconfigurable brain-inspired processor with the capacity to learn based on input data. The hardware developed as part of this proposal will be used to train and motivate the next generation of students in the areas of microelectronics. Further, the hardware developed will use open-source computer aided design tools to enable broader dissemination of the developed technology. The study proposes to develop a reconfigurable mixed-signal neuromorphic hardware for processing data at the extreme edge. This proposal aims to increase the energy efficiency of neuromorphic hardware by employing mixed-signal circuits to model the neurons and synapses. Further, we propose to incorporate programmable mixed-signal circuit topologies and explore techniques to co-optimize the hardware and software models to learn and adapt the network parameters in the presence of mismatch and variations. In addition, the proposal will also explore circuit topologies to perform learning on-chip. Based on these individual elements, the proposal will investigate a system architecture to develop reconfigurable hardware that can compile a spiking neural network with the capability to learn on-chip for performing extreme edge tasks. The extreme edge task we plan to validate the developed hardware is object detection using openly available datasets and a quadcopter with a limited battery capacity to perform object detection. The proposal will address the knowledge gaps in designing a learning algorithm for a mixed-signal spiking neural network with variation and mismatch, circuit topologies and system architecture for performing on-chip learning with limited resources, and advance the state-of-the-art in energy-efficient neuromorphic hardware.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.