  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.
