论文标题
S4OC：一种自我优化的自我适应安全的芯片系统设计框架，以应对未知威胁 - 网络理论，学习方法
S4oC: A Self-optimizing, Self-adapting Secure System-on-Chip Design Framework to Tackle Unknown Threats -- A Network Theoretic, Learning Approach
论文作者
论文摘要
我们提出了一个框架，以设计和优化安全的自我优化，自动化系统芯片（S4OC）体系结构。目的是通过实时调整来最大程度地减少硬件特洛伊木马和侧渠道等攻击的影响。 S4OC学会了重新配置自己，但要受到各种安全措施和攻击，其中一些在设计时可能未知。此外，还包括了目标应用程序，环境条件和变化来源的数据类型和模式。 S4OC是一个多核系统，以四层图建模，代表计算模型（MOCP），连接模型（MOCN），内存模型（MOM）和存储模型（MOS）（MOS），包括大量元素，包括MOCP中的异构重新配置元素，以及MOM层中的存储元素。安全驱动的社区检测和神经网络用于应用程序任务群集，并用于任务映射的分布式增强学习（RL）。
We propose a framework for the design and optimization of a secure self-optimizing, self-adapting system-on-chip (S4oC) architecture. The goal is to minimize the impact of attacks such as hardware Trojan and side-channel, by making real-time adjustments. S4oC learns to reconfigure itself, subject to various security measures and attacks, some of which possibly unknown at design time. Furthermore, the data types and patterns of the target applications, environmental conditions, and sources of variations are incorporated. S4oC is a manycore system, modeled as a four-layer graph, representing the model of computation (MoCp), model of connection (MoCn), model of memory (MoM) and model of storage (MoS), with a large number of elements including heterogeneous reconfigurable processing elements in MoCp, and memory elements in the MoM layer. Security driven community detection, and neural networks are utilized for application task clustering, and distributed reinforcement learning (RL) for task mapping.
