Emerging Threats and Countermeasures in Neuromorphic Systems: A Survey

TL;DR

This survey reviews security threats and countermeasures in neuromorphic systems, emphasizing attack methods, vulnerabilities, and protective strategies in hardware and software, to foster trustworthy brain-inspired computing architectures.

Contribution

It systematically analyzes security challenges in neuromorphic computing, integrating attack methodologies and countermeasures, and highlights the role of hardware primitives like PUFs and TRNGs.

Findings

Identification of new attack surfaces in neuromorphic architectures

Analysis of side-channel vulnerabilities in SNN hardware

Discussion of countermeasures balancing efficiency and security

Abstract

Neuromorphic computing mimics brain-inspired mechanisms through spiking neurons and energy-efficient processing, offering a pathway to efficient in-memory computing (IMC). However, these advancements raise critical security and privacy concerns. As the adoption of bio-inspired architectures and memristive devices increases, so does the urgency to assess the vulnerability of these emerging technologies to hardware and software attacks. Emerging architectures introduce new attack surfaces, particularly due to asynchronous, event-driven processing and stochastic device behavior. The integration of memristors into neuromorphic hardware and software implementations in spiking neural networks offers diverse possibilities for advanced computing architectures, including their role in security-aware applications. This survey systematically analyzes the security landscape of neuromorphic systems, covering attack methodologies, side-channel vulnerabilities, and countermeasures. We focus on both hardware and software concerns relevant to spiking neural networks (SNNs) and hardware primitives, such as Physical Unclonable Functions (PUFs) and True Random Number Generators (TRNGs) for cryptographic and secure computation applications. We approach this analysis from diverse perspectives, from attack methodologies to countermeasure strategies that integrate efficiency and protection in brain-inspired hardware. This review not only maps the current landscape of security threats but provides a foundation for developing secure and trustworthy neuromorphic architectures.