Automated Information Flow Analysis for Integrated Computing-in-Memory Modules

Novel non-volatile memory (NVM) technologies offer high-speed and high-density data storage. In addition, they overcome the von Neumann bottleneck by enabling computing-in-memory (CIM). Various computer architectures have been proposed to integrate CIM blocks in their design, forming a mixed-signal...

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Bibliographic Details
Published in2023 21st IEEE Interregional NEWCAS Conference (NEWCAS) pp. 1 - 5
Main Authors Reimann, Lennart M., Staudigl, Felix, Leupers, Rainer
Format Conference Proceeding
LanguageEnglish
Published IEEE 26.06.2023
Subjects
confidentiality
Design automation
Hardware security
information flow analysis
Memristors
neuromorphic computing
Neuromorphics
Nonvolatile memory
Protocols
Static analysis
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Summary:Novel non-volatile memory (NVM) technologies offer high-speed and high-density data storage. In addition, they overcome the von Neumann bottleneck by enabling computing-in-memory (CIM). Various computer architectures have been proposed to integrate CIM blocks in their design, forming a mixed-signal system to combine the computational benefits of CIM with the robustness of conventional CMOS. Novel electronic design automation (EDA) tools are necessary to design and manufacture these so-called neuromorphic systems. Furthermore, EDA tools must consider the impact of security vulnerabilities, as hardware security attacks have increased in recent years. Existing information flow analysis (IFA) frameworks offer an automated tool-suite to uphold the confidentiality property for sensitive data during the design of hardware. However, currently available mixed-signal EDA tools are not capable of analyzing the information flow of neuromorphic systems. To illustrate the shortcomings, we develop information flow protocols for NVMs that can be easily integrated in the already existing tool-suites. We show the limitation of the state-of-the-art by analyzing the flow from sensitive signals through multiple memristive crossbar structures to potential untrusted components and outputs. Finally, we provide a thorough discussion of the merits and flaws of the mixed-signal IFA frameworks on neuromorphic systems.
ISSN:2474-9672
DOI:10.1109/NEWCAS57931.2023.10198075