CNS Core: Small: Online Performance Monitoring of Neuromorphic Services

通过尖峰神经网络（snn）实现的机器学习应用程序可以在一种称为神经形态处理器的新型计算机体系结构上以很少的能量执行。这些处理器模拟大脑中生物神经元和突触的结构和操作，特别适合执行snn，其计算由网络中发生的峰值的位置和频率指导。对于传统的处理器来说，检查程序生成结果的正确性是很容易理解的。该项目开发技术来检查snn在神经形态处理器上执行的结果的正确性。该项目的智力优势在于为神经形态处理器开发了一个在线性能监测框架。该框架包括重复和比较，以及低成本的基于模型的方法来检测影响这些处理器内部电路的故障。性能监控单元从这些电路中收集实时行为数据，以峰值时间为单位，为后续分析提供分析模型。利用周期精确仿真和神经形态硬件的故障注入实验，从硬件和能源成本、执行snn的入侵开销和故障覆盖率等方面判断监测框架的有效性。该项目开发的工具和技术将促进神经形态计算在美国更广泛的科学和工程界的使用，保持在机器学习和人工智能方面的领导地位。该项目通过德雷塞尔大学的垂直整合项目项目让本科生参与研究。来自学术界和工业界的合作伙伴提供关于神经形态计算研究和发展的客座讲座，这些讲座被整合到相关课程中。与尤里卡计划合作！该项目还使高中女生参与计算机编程，目的是扩大对计算机的参与。包含SNN模拟器、性能监视器和分析模型的代码库是开源的，并将保持五年。软件交付成果，包括模拟器的稳定版本，将通过公共存储库提供给更广泛的研究社区。该存储库还将包含各种snn基准测试应用程序及其执行跟踪，以及教程和文档。该资源库的网址是https://github.com/drexel-DISCO.This，该奖项反映了NSF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。

Machine learning applications that are implemented via spiking neural networks (SNNs) can be executed using very little energy on a new type of computer architecture called neuromorphic processors. These processors mimic the structure and operation of biological neurons and synapses within the brain and are especially suitable for executing SNNs whose computations are guided by the location and frequency of spikes occurring within the network. Checking the correctness of results generated by a program is well understood for traditional processors. This project develops techniques to check the correctness of results generated by SNNs executing on neuromorphic processors. The intellectual merits of the project lie in the development of an online performance monitoring framework for neuromorphic processors. This framework includes both duplicate and compare, as well as low-cost model-based approaches to detect faults affecting the internal circuitry of these processors. Performance monitoring units collect real-time behavioral data from these circuits, in terms of spike times, to feed the analytical models for subsequent analysis. Efficacy of the monitoring framework is judged in terms of hardware and energy costs, intrusion overhead on executing SNNs, and fault coverage, using cycle-accurate simulations as well as fault-injection experiments on neuromorphic hardware.Tool and techniques developed by this project will promote use of neuromorphic computing within the broader science and engineering community in the United States, sustaining the leadership role in machine learning and artificial intelligence. The project involves undergraduate students in research via the Vertically Integrated Projects program at Drexel University. Collaborators from academia and industry deliver guest lectures on research and development in neuromorphic computing, with these lectures being integrated within relevant courses. Partnering with Project Eureka! the project also engages high-school girls in computer programming with the aim of broadening participation in computing. The code base containing the SNN simulators, performance monitors, along with the analytical models is open source and will be maintained as such for five years. Software deliverables, including stable releases of the simulators, will be made available to the broader research community via a public repository. This repository will also contain the various SNN-benchmark applications and their execution traces, as well as tutorials and documentation. The URL for the repository is https://github.com/drexel-DISCO.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.