Reconfigurable Neuromorphic Computing to enable Energy-Efficient Edge Intelligence

可重构神经形态计算实现节能边缘智能

• 批准号: 2210804
• 负责人: Sahil Shah
• 金额: $ 33.62万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2025-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2210804&HistoricalAwards=false
• 关键词: Reconfigurable; Neuromorphic; Computing; enable; Energy

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.

该项目的目标是从人脑中汲取灵感，开发一种节能且智能的极限边缘设备。极端边缘设备可以实现多种应用。具体来说，这些设备可用于远程跟踪北极的快速变化、用于深空探索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，当前的技术需要在云端处理遥感数据。该框架有几个缺点，例如感测和决策之间的延迟、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先进技术。具体来说，这笔赠款将有助于开发可重构的类脑处理器，该处理器具有基于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源计算机辅助设计工具，以便更广泛地传播所开发的技术。 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。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源效率。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调整网络参数。此外，该提案还将探索电路拓扑以进行片上学习。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以执行极端边缘任务的尖峰神经网络。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来执行物体检测的物体检测。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，以在有限的资源下执行片上学习，并推进最先进的技术该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。