Visible to the public Low Power and Trusted Machine Learning

TitleLow Power and Trusted Machine Learning
Publication TypeConference Paper
Year of Publication2018
AuthorsSasan, Avesta, Zu, Qi, Wamg, Yanzhi, Seo, Jae-sun, Mohsenin, Tinoosh
Conference NameProceedings of the 2018 on Great Lakes Symposium on VLSI
Conference LocationNew York, NY, USA
ISBN Number978-1-4503-5724-1
Keywordscomposability, low power neuromorphic computing, machine learning, neuromorphic hardware acceleration, neuromorphic hardware security, pubcrawl, security scalability

In this special discussion session on machine learning, the panel members discuss various issues related to building secure and low power neuromorphic systems. The security of neuromorphic systems may be discussed in term of the reliability of the model, trust in the model, and security of the underlying hardware. The low power aspect of neuromorphic computing systems may be discussed in terms of adaptation of new devices and technologies, the adaptation of new computational models, development of heterogeneous computing frameworks, or dedicated engines for processing neuromorphic models. This session may include discussion on the design space of such supporting hardware, exploring tradeoffs between power/energy, security, scalability, hardware area, performance, and accuracy.

Citation Keysasan_low_2018