Reconfigurable Neuromorphic Computing to enable Energy-Efficient Edge Intelligence

这个项目的目标是通过从人类大脑中获取灵感，开发一种节能和智能的极限设备。极端边缘设备可以启用各种应用程序。具体来说，这些设备可用于远程跟踪北极地区的快速变化，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。该框架有几个缺点，例如传感和决策之间的延迟，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先前提到的挑战，并将推进最先进的极端边缘设备。具体来说，这笔拨款将用于开发一种可重构的大脑启发处理器，该处理器具有根据输入数据进行学习的能力。作为该提案一部分的硬件开发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源计算机辅助设计工具，使开发的技术能够更广泛地传播。该研究建议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。本研究旨在利用混合信号电路来模拟神经元和突触，以提高神经形态硬件的能量效率。此外，我们建议结合可编程混合信号电路拓扑，并探索共同优化硬件和软件模型的技术，以在存在不匹配和变化的情况下学习和适应网络参数。此外，该提案还将探索电路拓扑以执行片上学习。基于这些单独的元素，该提案将研究一种系统架构，以开发可重构硬件，该硬件可以编译具有在芯片上学习能力的脉冲神经网络，以执行极端边缘任务。我们计划验证开发硬件的极端边缘任务是使用公开可用的数据集和具有有限电池容量的四轴飞行器来执行目标检测。该提案将解决在设计具有变化和不匹配的混合信号尖峰神经网络的学习算法，电路拓扑和系统架构方面的知识差距，用于在有限资源下进行片上学习，并推进节能神经形态硬件的最新技术。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.