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

• Published in: IEEE International Symposium on Circuits and Systems proceedings pp. 1 - 8
• Main Authors: Nazarian, Shahin, Bogdan, Paul
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.10.2020
• Subjects: Community detection; Computational modeling; Computer architecture; distributed RL; heterogeneous manycore; machine learning; Method of moments; multi-valued logic PUF; Real-time systems; Security; System-on-chip; Task analysis; ternary computing
• ISBN: 9781728133201; 1728133203
• ISSN: 2158-1525
• DOI: 10.1109/ISCAS45731.2020.9180687

We propose a framework for the design and optimization of a secure self-optimizing, self-adapting system-on-chip (S 4 oC) architecture. The goal is to minimize the impact of attacks such as hardware Trojan and side-channel, by making real-time adjustments. S 4 oC 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. S 4 oC 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.