Special Session: Towards an Agile Design Methodology for Efficient, Reliable, and Secure ML Systems
Special Session: Towards an Agile Design Methodology for Efficient, Reliable, and Secure ML Systems
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DOI：
10.1109/vts52500.2021.9794253
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发表时间：
2022-04
期刊：
影响因子：
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通讯作者：
Shail Dave;Alberto Marchisio;M. Hanif;Amira Guesmi;Aviral Shrivastava;Ihsen Alouani;Muhammad Shafique
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文献类型：
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作者：
Shail Dave;Alberto Marchisio;M. Hanif;Amira Guesmi;Aviral Shrivastava;Ihsen Alouani;Muhammad Shafique
在过去的几年里，机器学习（ML）的真实用例已经爆炸式增长。然而，当前的计算基础设施不足以支持所有现实世界的应用和场景。除了高效率要求外，现代机器学习系统还有望在硬件故障方面具有高度可靠性，并能抵御对抗性攻击和IP窃取攻击。隐私问题也正在成为一个首要问题。本文总结了敏捷开发高效、可靠和安全的ML系统所面临的主要挑战，然后概述了一种敏捷设计方法，以基于用户定义的约束和目标生成高效、可靠和安全的ML系统。
The real-world use cases of Machine Learning (ML) have exploded over the past few years. However, the current computing infrastructure is insufficient to support all real-world applications and scenarios. Apart from high efficiency requirements, modern ML systems are expected to be highly reliable against hardware failures as well as secure against adversarial and IP stealing attacks. Privacy concerns are also becoming a first-order issue. This article summarizes the main challenges in agile development of efficient, reliable and secure ML systems, and then presents an outline of an agile design methodology to generate efficient, reliable and secure ML systems based on user-defined constraints and objectives.