Enhancing Functional Safety in Automotive AMS Circuits through
  Unsupervised Machine Learning
Ayush Arunachalam Ian Kintz Suvadeep Banerjee Arnab Raha Xiankun Jin Fei Su Viswanathan Pillai Prasanth Rubin A. Parekhji Suriyaprakash Natarajan Kanad Basu
Given the widespread use of safety-critical applications in the automotive
field, it is crucial to ensure the Functional Safety (FuSa) of circuits and
components within automotive systems. The Analog and Mixed-Signal (AMS)
circuits prevalent in these systems are more vulnerable to faults induced by
parametric perturbations, noise, environmental stress, and other factors, in
comparison to their digital counterparts. However, their continuous signal
characteristics present an opportunity for early anomaly detection, enabling
the implementation of safety mechanisms to prevent system failure. To address
this need, we propose a novel framework based on unsupervised machine learning
for early anomaly detection in AMS circuits. The proposed approach involves
injecting anomalies at various circuit locations and individual components to
create a diverse and comprehensive anomaly dataset, followed by the extraction
of features from the observed circuit signals. Subsequently, we employ
clustering algorithms to facilitate anomaly detection. Finally, we propose a
time series framework to enhance and expedite anomaly detection performance.
Our approach encompasses a systematic analysis of anomaly abstraction at
multiple levels pertaining to the automotive domain, from hardware- to
block-level, where anomalies are injected to create diverse fault scenarios. By
monitoring the system behavior under these anomalous conditions, we capture the
propagation of anomalies and their effects at different abstraction levels,
thereby potentially paving the way for the implementation of reliable safety
mechanisms to ensure the FuSa of automotive SoCs. Our experimental findings
indicate that our approach achieves 100% anomaly detection accuracy and
significantly optimizes the associated latency by 5X, underscoring the
effectiveness of our devised solution.