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

Autor:	Nazarian, Shahin, Bogdan, Paul
Rok vydání:	2020
Předmět:	Computer Science - Distributed Parallel and Cluster Computing Computer Science - Hardware Architecture Computer Science - Cryptography and Security
Druh dokumentu:	Working Paper
Popis:	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. Comment: This is an invited paper to ISCAS 2020
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2004.02109 Zobrazit plný text záznamu View this record from Arxiv