CAREER: Analog Memory Leaks: Mitigating Remanence Side-Channels in Analog Compute-in-Memory Hardware

Next-generation artificial intelligence systems use analog arithmetic to vastly improve efficiency over traditional digital artificial intelligence systems. While the energy efficiency benefits of these analog systems are known, the security vulnerabilities of these new computing architectures are not. Digital systems are built on top of a wealth of defenses and research in cybersecurity, but they do not necessarily apply to analog systems. This project’s novelties are developing guidelines and mitigations for security vulnerabilities in analog computing systems. The project's broader significance and importance are increasing security by addressing vulnerabilities in future artificial intelligence systems, especially in analog computer security. The education and outreach plan addresses an existing and urgent chip design workforce shortage in the United States through increasing the number of digital design classrooms. This project studies next-generation artificial intelligence systems that use novel analog compute-in-memory circuits. Data storage, in normal operation, causes wearout in on-chip storage circuits that change fundamental device parameters like resistance and capacitance. The analog nature of these systems implies that wear, caused by storage (memory), is visible, and can be used to form a side-channel between a user and an adversary. This project will study wearout in a broad spectrum of memory technologies using physics-based simulators and off-the-shelf systems. Ultimately, this project will develop mitigations for current and future artificial intelligence systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.