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The paper addresses some of the opportunities and challenges related to test and reliability of three major emerging computing paradigms; i.e., Quantum Computing, Computing engines based on Deep Neural Networks for AI, and Approximate Computing (AxC). We present a quantum accelerator showing that it can be done even without the presence of very good qubits. Then, we present Dependability for Artificial Intelligence (AI) oriented Hardware. Indeed, AI applications shown relevant resilience properties to faults, meaning that the testing strongly depends on the application behavior rather than on the hardware structure. We will cover AI hardware design issues due to manufacturing defects, aging faults, and soft errors. Finally, We present the use of AxC to reduce the cost of hardening a digital circuit without impacting its reliability. In other words how to go beyond usual modular redundancy scheme.
Bosio et al. (Mon,) studied this question.