Novel Semiconductor Chip Design with Functional Safety and Cyber Security Capability for AI-ML Based Software Defined Zonal Vehicle Architecture

Abstract

This article presents a detailed exploration of functional safety semiconductor chip design for automotive applications with Functional safety and cyber security capability, focusing on Software-Defined Vehicle (SDV) architectures. The integration of AI/ML accelerators in semiconductor chips is examined to enhance real-time performance, safety compliance, and predictive fault detection. With the increasing complexity of modern automotive systems, meeting (ASIL-B) Automotive Safety Integrity Level B standards is paramount for ensuring reliability and safety in critical systems. We explore the design of a novel semiconductor chip capable of seamlessly integrating AI/ML models, including deep learning, reinforcement learning, and anomaly detection, to predict, identify, and mitigate faults in real-time. The article also emphasizes the importance of fault tolerance mechanisms, including redundant cores and N-Modular Redundancy (NMR), to ensure continued operation during a failure. Furthermore, we discuss how AI-ML accelerators, such as ASICs, TPUs, and FPGAs, can significantly improve processing efficiency, reduce latency, and enhance energy efficiency within the automotive environment. Key considerations, such as cyber security, are also addressed to protect against potential system vulnerabilities while maintaining compliance with ASIL-B safety requirements.