Advanced Circuit Techniques for Secure Analog Neural Implementations

Abstract

With the rise of artificial intelligence (AI) and neural network models in various sectors, securing the hardware implementations of neural networks has become increasingly critical. Analog neural networks, known for their power efficiency and high-speed computations, offer a promising solution for AI-based applications. However, the integration of neural processing units (NPUs) in analog circuits raises significant concerns about security, privacy, and integrity. This paper explores advanced circuit techniques designed to enhance the security of analog neural network implementations. These methods address vulnerabilities such as signal leakage, circuit manipulation, and adversarial attacks, which can compromise the performance and integrity of neural networks. The focus is on circuit-level innovations, including secure training methods, hardware obfuscation, side-channel attack mitigation, and tamper detection. By leveraging advanced design techniques, this work seeks to pave the way for more secure and reliable analog hardware for neural network applications, particularly in critical areas such as finance, healthcare, and defense.