@article {zhu2017cognitive,
	title = {Cognitive Computing Safety: The New Horizon for Reliability/The Design and Evolution of Deep Learning Workloads},
	journal = {IEEE Micro},
	number = {1},
	year = {2017},
	pages = {15{\textendash}21},
	publisher = {IEEE},
	abstract = {Recent advances in cognitive computing have brought widespread excitement for various machine learning{\textendash}based intelligent services, ranging from autonomous vehicles to smart traffic-light systems. To push such cognitive services closer to reality, recent research has focused extensively on improving the performance, energy efficiency, privacy, and security of cognitive computing platforms.Among all the issues, a rapidly rising and critical challenge to address is the practice of safe cognitive computing{\textemdash} that is, how to architect machine learning{\textendash}based systems to be robust against uncertainty and failure to guarantee that they perform as intended without causing harmful behavior. Addressing the safety issue will involve close collaboration among different computing communities, and we believe computer architects must play a key role. In this position paper, we first discuss the meaning of safety and the severe implications of the safety issue in cognitive computing. We then provide a framework to reason about safety, and we outline several opportunities for the architecture community to help make cognitive computing safer.},
	url = {https://doi.org/10.1109/MM.2017.2},
	author = {Zhu, Yuhao and Reddi, Vijay Janapa and Adolf, Robert and Rama, Saketh and Reagen, Brandon and Gu-Yeon Wei and David Brooks}
}
