Survey of Security Issues in Memristor-Based Machine Learning Accelerators for RF Analysis

Abstract

We explore security aspects of a new computing paradigm that combines novel memristors and traditional Complimentary Metal Oxide Semiconductor (CMOS) to construct a highly efficient analog and/or digital fabric that is especially well-suited to Machine Learning (ML) inference processors for Radio Frequency (RF) signals. Analog and/or hybrid hardware designed for such application areas follows different constraints from that of traditional CMOS. This paradigm shift allows for enhanced capabilities but also introduces novel attack surfaces. Memristors have different properties than traditional CMOS which can potentially be exploited by attackers. In addition, the mixed signal approximate computing model has different vulnerabilities than traditional digital implementations. However both the memristor and the ML computation can be leveraged to create security mechanisms and countermeasures ranging from lightweight cryptography, identifiers (e.g., Physically Unclonable Functions (PUFs), fingerprints, and watermarks), entropy sources, hardware obfuscation and leakage/attack detection methods. Three different threat models are proposed: (1) Supply Chain, (2) Physical Attacks, and (3) Remote Attacks. For each threat model, potential vulnerabilities and defenses are identified. This survey reviews a variety of recent work from the hardware and ML security literature and proposes open problems for both attack and defense. The survey emphasizes the growing area of RF signal analysis and identification in terms of commercial space, as well as military applications and threat models. We differ from other recent surveys that target ML, in general, neglecting RF applications.

Keywords

• memristors
• eNVM
• compute-in-memory
• analog-edge processors
• RF
• machine learning