AI is now sufficiently mature. However, it is still unclear whether we can trust AI to perform critical tasks and we are not yet convinced it deserves a driving licence. Despite the questions about AI reliability, driverless cars are the new trend in the automotive market and, to burst deep space exploration, NASA and ESA are working on adding self-driving capabilities to their rovers. In the talk, after a brief description of radiation effects at physical level, we will investigate the reliability of modern and emerging computing architectures required to execute neural networks, such as Graphics Processing Units (GPUs), dedicated accelerators such as Tensor Processing Units (TPUs), Field Programmable Gate Arrays (FPGAs), or even post Von-Neumann devices such as Processing In Memory (PIM) or Neuromorphic devices. We will show if and why a radiation-induced corruption can modify the autonomous vehicles behaviors, and discuss the implications of these corruptions for the adoption of self-driving vehicles in safety-critical applications. The reliability evaluation, to be accurate and precise, is based on the combination of beam experiments and fault injection at different levels of abstractions (RTL, microarchitectural, and software). This combination allows us to have a realistic evaluation of the error rate, distinguish between tolerable errors and critical errors, and to design efficient and effective hardening solutions for neural networks. Finally, we will discuss how, by exploiting the potential of machine learning and taking full advantage of the computing resources in modern accelerators, it is possible to significantly improve the neural network reliability with nearly-zero overhead.

Quantum computing is a new computational paradigm, expected to revolutionize the computing field in the next few years. Qubits, the atomic units of a quantum circuit, exploit the quantum physics properties to increase the parallelism and speed of computation. Unfortunately, qubits are both intrinsically noisy and highly susceptible to external sources of faults, such as ionizing radiation. The reported qubits error rate is so high that researchers are questioning the large-scale adoption of quantum computers and are forced to implement unpractical mitigation solutions such as installing the quantum computer in underground caves. Innovative solutions to improve the reliability of quantum applications are then highly necessary. In the talk, after providing all information and background needed to understand quantum computing basics and an overview of the available quantum technologies vulnerabilities, we will present the available hardening solutions and the open challenges that need to be addressed. We will consider both the intrinsic noise, that has a predictable and incremental effect, and radiation-induced transient faults, that are stochastic and modify the qubit in an unpredictable way. Based on the latest studies and radiation experiments performed on real quantum machines, we will show how to model the transient faults in a qubit and how to inject this fault in a quantum circuit to track its propagation. We will discuss the vulnerability of qubits and of circuits, identifying the most critical parts and the main course for output corruption, and show how to design effective Quantum Error Correction circuits. Finally, we will provide an overview of the open (reliability) challenges in quantum computing to stimulate further studies and solutions.