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.

翻译：新型非易失性存储器（NVM）技术能够实现高速高密度数据存储，此外，它们通过支持存内计算（CIM）克服了冯·诺依曼瓶颈。研究人员已提出多种计算机架构，将CIM模块集成至设计中，形成混合信号系统，以结合CIM的计算优势与传统CMOS的鲁棒性。设计并制造此类所谓神经形态系统需要新型电子设计自动化（EDA）工具。此外，由于近年来硬件安全攻击事件增多，EDA工具必须考虑安全漏洞的影响。现有的信息流分析（IFA）框架提供了一套自动化工具套件，可在硬件设计过程中维护敏感数据的机密性属性。然而，当前可用的混合信号EDA工具尚无法分析神经形态系统的信息流。为阐明这一不足，我们为NVM开发了可轻松集成至现有工具套件中的信息流协议。通过分析敏感信号经多个忆阻交叉开关结构至潜在不可信组件与输出的流通过程，我们展示了现有技术的局限性。最后，我们针对神经形态系统上混合信号IFA框架的优缺点进行了深入探讨。