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We propose a framework for the design and optimization of a secureself-optimizing, self-adapting system-on-chip (S4oC) architecture. The goal isto minimize the impact of attacks such as hardware Trojan and side-channel, bymaking real-time adjustments. S4oC learns to reconfigure itself, subject tovarious security measures and attacks, some of which possibly unknown at designtime. Furthermore, the data types and patterns of the target applications,environmental conditions, and sources of variations are incorporated. S4oC is amanycore system, modeled as a four-layer graph, representing the model ofcomputation (MoCp), model of connection (MoCn), model of memory (MoM) and modelof storage (MoS), with a large number of elements including heterogeneousreconfigurable processing elements in MoCp, and memory elements in the MoMlayer. Security driven community detection, and neural networks are utilizedfor application task clustering, and distributed reinforcement learning (RL)for task mapping.

S4oC: A Self-optimizing, Self-adapting Secure System-on-Chip Design Framework to Tackle Unknown Threats -- A Network Theoretic, Learning Approach