The increasing adoption of artificial intelligence (AI)-based systems has led to growing concerns about their deployment in safety-critical environments. Industry standards, such as ISO 26262 for the automotive sector, mandate the detection of hardware faults during the device's canonical operations. Similarly, new standards are emerging to address the functional safety of AI systems (e.g., ISO/IEC CD TR 5469). Hardware solutions have been proposed for in-field testing of the hardware executing AI applications, nevertheless these approaches can increase hardware costs and may potentially negatively impact the strive for performance maximization, especially in applications involving Convolutional Neural Networks (CNNs) for image processing tasks.