Identification of Important FPGA Modules Based on Complex Network
Source abstract
Abstract
The globalization of hardware designs and supply chains, as well as the integration of third-party intellectual property (IP) cores, has led to an increased focus from malicious attackers on computing hardware. However, existing defense or detection approaches often require additional circuitry to perform security verification, and are thus constrained by time and resource limitations. Considering the scale of actual engineering tasks and tight project schedules, it is usually difficult to implement designs for all modules in field programmable gate array (FPGA) circuits. Some studies have pointed out that the failure of key modules tends to cause greater damage to the network. Therefore, under limited conditions, priority protection designs need to be made on key modules to improve protection efficiency. We have conducted research on FPGA designs including single FPGA systems and multi-FPGA systems, to identify key modules in FPGA systems. For the single FPGA designs, considering the topological structure, network characteristics, and directionality of FPGA designs, we propose a node importance evaluation method based on the technique for order preference by similarity to an ideal solution (TOPSIS) method. Then, for the multi-FPGA designs, considering the influence of nodes in intra-layer and inter-layers, they are constructed into the interdependent network, and we propose a method based on connection strength to identify the important modules. Finally, we conduct empirical research using actual FPGA designs as examples. The results indicate that compared to other traditional indexes, node importance indexes proposed for different designs can better characterize the importance of nodes.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Physical Unclonable Functions (PUFs) and Hardware Security · Computational Drug Discovery Methods · Advanced Memory and Neural Computing
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (38)
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