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CNS Core: Small: Online Performance Monitoring of Neuromorphic Services

CNS 核心：小型：神经形态服务的在线性能监控

基本信息

批准号：
2008167
负责人：
Nagarajan Kandasamy
金额：
$ 49.41万
依托单位：
Drexel University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-10-01 至 2024-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2008167&HistoricalAwards=false
关键词：
CNS Core Small Online Performance

项目摘要

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.

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架构上使用非常少的能量来执行。这些处理器模拟大脑内生物神经元和突触的结构和操作，并且特别适合于执行SNN，其计算由网络内发生的尖峰的位置和频率引导。检查程序生成的结果的正确性对于传统处理器来说是很好理解的。该项目开发的技术，以检查SNN上执行神经形态处理器产生的结果的正确性。该项目的智力价值在于开发一个神经形态处理器的在线性能监控框架。该框架包括复制和比较，以及低成本的基于模型的方法来检测影响这些处理器的内部电路的故障。性能监控单元从这些电路收集实时行为数据，以尖峰时间为单位，为后续分析提供分析模型。监测框架的有效性是判断的硬件和能源成本，执行SNN的入侵开销，和故障覆盖率，使用周期准确的模拟以及故障注入实验neuromorphic hardware.Tool和技术开发的这个项目将促进使用neuromorphic计算在更广泛的科学和工程界在美国，保持在机器学习和人工智能领域的领导地位。该项目涉及通过垂直综合项目在德雷克塞尔大学研究的本科生。来自学术界和工业界的合作者提供关于神经形态计算研究和开发的客座讲座，这些讲座被整合到相关课程中。与尤里卡项目合作！该项目还让高中女生参与计算机编程，目的是扩大对计算的参与。包含SNN模拟器、性能监视器沿着分析模型的代码库是开源的，并将维护五年。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界。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。存储库的URL是https://github.com/drexel-DISCO.This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。