arXiv subjects

Paolo Rech

Publications and source records attributed to Paolo Rech.

16 recordsLinked to original sources

arXiv2026

TETRIS-Q: Tiling-based Effective Transient-fault Reduction on Interleaved Superconducting Qubits

The struggle of the hour in quantum computing research is achieving effective suppression of the error mechanisms induced by the interaction of external radiation with superconducting quantum devices. Despite the rapid advancements in quantum error correction (QEC) of recent years, radiation-induced faults are yet to be fully addressed. These events are known to be the cause of simultaneous correlated defects in qubits that lie onto a single substrate, ultimately jeopardising QEC code effectiveness. In this paper, we propose to selectively combine substrate-level phonon barriers and QEC interleaving via a planar-mesh tiling algorithm, TETRIS-Q, reaching efficient and effective suppression of radiation events. Our cross-layer solution comes at no extra cost in terms of QEC code execution or decoding time. We model and simulate radiation-induced transient faults over a plethora of barrier and QEC interleaving configurations. Through more than 51 million quantum circuit simulations, we show peak logical error reductions of more than $99.8 \%$, together with an $80\%$ reduction of the observable transient duration with permeable barriers. We find that sparser tiling can reach comparable performance to single qubit tiling, prompting cost reductions of upwards of $87 \%$ in barrier tracing. By leveraging independent QEC code interleaving, we measure up to one order of magnitude average logical error rate reductions without the use of permeable barriers, and up to three orders of magnitude with the joint usage of barriers.

quant-ph↗

arXiv2025

Architecture, Simulation and Software Stack to Support Post-CMOS Accelerators: The ARCHYTAS Project

ARCHYTAS aims to design and evaluate non-conventional hardware accelerators, in particular, optoelectronic, volatile and non-volatile processing-in-memory, and neuromorphic, to tackle the power, efficiency, and scalability bottlenecks of AI with an emphasis on defense use cases (e.g., autonomous vehicles, surveillance drones, maritime and space platforms). In this paper, we present the system architecture and software stack that ARCHYTAS will develop to integrate and support those accelerators, as well as the simulation software needed for early prototyping of the full system and its components.

cs.AR↗

arXiv2025

SQUID G.A.M.E.: Gamma, Atmospheric, and Mono-Energetic Neutron Effects on Quantum Devices

Quantum devices are a promising solution to many research applications, including medical imaging, precision magnetic field measurements, condensed matter physics, and overcoming the limits of classical computing. Among the available implementations, the superconducting technology is the current focus of scientific research and industrial applications, excelling in performance and scalability. Despite this, superconducting quantum systems are extremely prone to decoherence, and in particular, they are highly sensitive to radiation events. In this paper, we analyze the response of a superconducting device (SQUID) to radiation. We expose the SQUID to beams of monoenergetic 14 MeV neutrons (NILE - ISIS), atmospheric 1-800 MeV neutrons (ChipIR - ISIS), and gamma rays with 1.25 MeV average energy (CALLIOPE - ENEA). These experiments show that the SQUID is sensitive to the two neutron fields, while gamma rays at 1.25 MeV leave it mostly unaffected. Following our experiments with neutrons, it is possible to characterize the SQUID's response and even classify faults according to their shape and duration. We identify two categories: bursts (long lasting) and peaks (short lived). To investigate the different responses to neutrons and gamma rays, we employ Geant4 simulations, which highlight differences in the deposition spectra and the energy propagation, but likewise predict the vulnerability of the SQUID in both cases.

quant-ph↗

arXiv2025

Radiation-Induced Fault Detection in Superconducting Quantum Devices

The quest for universal superconducting quantum computing is hindered by noise and errors. It has been proven that Quantum Error Correction (QEC) codes will lay at the foundation of fault tolerant quantum computing. However, cosmic-ray induced correlated errors, which are the most detrimental events that can impact superconducting quantum computers, are yet to be efficiently tackled. In order to reach fault tolerance, we must also develop radiation aware methods to complement QEC. In this paper, we propose the first algorithm to effectively exploit syndrome information for the efficient detection of radiation events in superconducting quantum devices at runtime. We perform a thorough analysis of simulated Rotated Surface codes injecting over 11 million physics-modeled radiation-induced faults. We consider the properties of the X and Z check bases, the impact of code distance, and the decoder's time to solution constraints. Our technique detects $100\%$ of injected faults, regardless of the impact's position. Moreover, we accurately identify both the radiation impact centre and the area affected, with an overhead lower than $0.3\%$ the decoding time. Additionally, we use the fault identification information to propose a radiation fault correction technique that improves of up to $20\%$ the output correctness compared to existing decoders.

quant-ph↗

arXiv2024

Transient Fault Tolerant Semantic Segmentation for Autonomous Driving

Deep learning models are crucial for autonomous vehicle perception, but their reliability is challenged by algorithmic limitations and hardware faults. We address the latter by examining fault-tolerance in semantic segmentation models. Using established hardware fault models, we evaluate existing hardening techniques both in terms of accuracy and uncertainty and introduce ReLUMax, a novel simple activation function designed to enhance resilience against transient faults. ReLUMax integrates seamlessly into existing architectures without time overhead. Our experiments demonstrate that ReLUMax effectively improves robustness, preserving performance and boosting prediction confidence, thus contributing to the development of reliable autonomous driving systems.

cs.CV↗

arXiv2024

Dependable Classical-Quantum Computer Systems Engineering

Quantum Computing (QC) offers the potential to enhance traditional High-Performance Computing (HPC) workloads by leveraging the unique properties of quantum computers, leading to the emergence of a new paradigm: HPC-QC. While this integration presents new opportunities, it also brings novel challenges, particularly in ensuring the dependability of such hybrid systems. This paper aims to identify integration challenges, anticipate failures, and foster a diverse co-design for HPC-QC systems by bringing together QC, cloud computing, HPC, and network security. The focus of this emerging inter-disciplinary effort is to develop engineering principles that ensure the dependability of hybrid systems, aiming for a more prescriptive co-design cycle. Our framework will help to prevent design pitfalls and accelerate the maturation of the QC technology ecosystem. Key aspects include building resilient HPC-QC systems, analyzing the applicability of conventional techniques to the quantum domain, and exploring the complexity of scaling in such hybrid systems. This underscores the need for performance-reliability metrics specific to this new computational paradigm.

quant-ph↗

arXiv2024

On the Efficacy of Surface Codes in Compensating for Radiation Events in Superconducting Devices

Reliability is fundamental for developing large-scale quantum computers. Since the benefit of technological advancements to the qubit's stability is saturating, algorithmic solutions, such as quantum error correction (QEC) codes, are needed to bridge the gap to reliable computation. Unfortunately, the deployment of the first quantum computers has identified faults induced by natural radiation as an additional threat to qubits reliability. The high sensitivity of qubits to radiation hinders the large-scale adoption of quantum computers, since the persistence and area-of-effect of the fault can potentially undermine the efficacy of the most advanced QEC. In this paper, we investigate the resilience of various implementations of state-of-the-art QEC codes to radiation-induced faults. We report data from over 400 million fault injections and correlate hardware faults with the logical error observed after decoding the code output, extrapolating physical-to-logical error rates. We compare the code's radiation-induced logical error rate over the code distance, the number and role in the QEC of physical qubits, the underlying quantum computer topology, and particle energy spread in the chip. We show that, by simply selecting and tuning properly the surface code, thus without introducing any overhead, the probability of correcting a radiation-induced fault is increased by up to 10\%. Finally, we provide indications and guidelines for the design of future QEC codes to further increase their effectiveness against radiation-induced events.

quant-ph↗

arXiv2024

Trikarenos: Design and Experimental Characterization of a Fault-Tolerant 28nm RISC-V-based SoC

RISC-V-based fault-tolerant system-on-chip (SoC) designs are critical for the new generation of automotive and space SoC architectures. However, reliability assessment requires characterization under controlled radiation doses to accurately quantify the fault tolerance of the fabricated designs. This work analyzes the Trikarenos design, a SoC implemented in TSMC 28nm, for single event upset (SEU) vulnerability under atmospheric neutron and 200 MeV proton radiation, comparing these results to simulation-based fault injection. All faults in error correction codes (ECC) protected memory are corrected by a scrubber, showing an estimated cross-section per bit of up to $1.09 \times 10^{-14}$ cm$^2$ bit$^{-1}$. Furthermore, the triple-core lockstep (TCLS) mechanism implemented in Trikarenos is validated and is shown to correct errors affecting a cross-section up to $3.23 \times 10^{-11}$ cm$^2$, with the remaining uncorrectable vulnerability below $5.36 \times 10^{-12}$ cm$^2$. When augmenting the experimental analysis of fabricated chips with gate-level fault injection in simulation, 99.10 % of injections into the SoC produced correct results, while 100 % of injections in the TCLS-protected cores were handled correctly. With 12.28 % of all injected faults leading to a TCLS recovery, this indicates an approximate effective flip-flop cross-section of up to $1.28 \times 10^{-14}$ cm$^2$/FF.

physics.ins-det↗

arXiv2024

State of practice: evaluating GPU performance of state vector and tensor network methods

The frontier of quantum computing (QC) simulation on classical hardware is quickly reaching the hard scalability limits for computational feasibility. Nonetheless, there is still a need to simulate large quantum systems classically, as the Noisy Intermediate Scale Quantum (NISQ) devices are yet to be considered fault tolerant and performant enough in terms of operations per second. Each of the two main exact simulation techniques, state vector and tensor network simulators, boasts specific limitations. The exponential memory requirement of state vector simulation, when compared to the qubit register sizes of currently available quantum computers, quickly saturates the capacity of the top HPC machines currently available. Tensor network contraction approaches, which encode quantum circuits into tensor networks and then contract them over an output bit string to obtain its probability amplitude, still fall short of the inherent complexity of finding an optimal contraction path, which maps to a max-cut problem on a dense mesh, a notably NP-hard problem. This article aims at investigating the limits of current state-of-the-art simulation techniques on a test bench made of eight widely used quantum subroutines, each in 31 different configurations, with special emphasis on performance. We then correlate the performance measures of the simulators with the metrics that characterise the benchmark circuits, identifying the main reasons behind the observed performance trend. From our observations, given the structure of a quantum circuit and the number of qubits, we highlight how to select the best simulation strategy, obtaining a speedup of up to an order of magnitude.

quant-ph↗

arXiv2023

Understanding the Effects of Permanent Faults in GPU's Parallelism Management and Control Units

Graphics Processing Units (GPUs) are over-stressed to accelerate High-Performance Computing applications and are used to accelerate Deep Neural Networks in several domains where they have a life expectancy of many years. These conditions expose the GPUs hardware to (premature) aging, causing permanent faults to arise after the usual end-of-manufacturing test. Techniques to assess the impact of permanent faults in GPUs are then strongly required, thus allowing to estimate the reliability risk and to possibly mitigate it. In this paper, we present a method to evaluate the effects of permanent faults affecting the GPU scheduler and control units, which are the most peculiar and stressed resources, along with the first figures that allow quantifying these effects. We characterize over 5.83x10^5 permanent fault effects in the scheduler and controllers of a gate-level GPU model. Then, we map the observed error categories in software by instrumenting the code of 13 applications and two convolutional neural networks, injecting more than 1.65x10^5 permanent errors. Our two-level fault injection strategy reduces the evaluation time from hundreds of years of gate-level evaluation to hundreds of hours.We found that faults in the GPU parallelism management units can modify the opcode, the addresses, and the status of thread(s) and warp(s). The large majority (up to 99%) of these hardware permanent errors impacts the running software execution. Errors affecting the instruction operation or resource management hang the code, while 45% of errors in the parallelism management or control-flow induce silent data corruptions.

cs.AR↗

arXiv2022

Characterizing a Neutron-Induced Fault Model for Deep Neural Networks

The reliability evaluation of Deep Neural Networks (DNNs) executed on Graphic Processing Units (GPUs) is a challenging problem since the hardware architecture is highly complex and the software frameworks are composed of many layers of abstraction. While software-level fault injection is a common and fast way to evaluate the reliability of complex applications, it may produce unrealistic results since it has limited access to the hardware resources and the adopted fault models may be too naive (i.e., single and double bit flip). Contrarily, physical fault injection with neutron beam provides realistic error rates but lacks fault propagation visibility. This paper proposes a characterization of the DNN fault model combining both neutron beam experiments and fault injection at software level. We exposed GPUs running General Matrix Multiplication (GEMM) and DNNs to beam neutrons to measure their error rate. On DNNs, we observe that the percentage of critical errors can be up to 61%, and show that ECC is ineffective in reducing critical errors. We then performed a complementary software-level fault injection, using fault models derived from RTL simulations. Our results show that by injecting complex fault models, the YOLOv3 misdetection rate is validated to be very close to the rate measured with beam experiments, which is 8.66x higher than the one measured with fault injection using only single-bit flips.

cs.AR↗

arXiv2022

Fault-Aware Design and Training to Enhance DNNs Reliability with Zero-Overhead

Deep Neural Networks (DNNs) enable a wide series of technological advancements, ranging from clinical imaging, to predictive industrial maintenance and autonomous driving. However, recent findings indicate that transient hardware faults may corrupt the models prediction dramatically. For instance, the radiation-induced misprediction probability can be so high to impede a safe deployment of DNNs models at scale, urging the need for efficient and effective hardening solutions. In this work, we propose to tackle the reliability issue both at training and model design time. First, we show that vanilla models are highly affected by transient faults, that can induce a performances drop up to 37%. Hence, we provide three zero-overhead solutions, based on DNN re-design and re-train, that can improve DNNs reliability to transient faults up to one order of magnitude. We complement our work with extensive ablation studies to quantify the gain in performances of each hardening component.

cs.LG↗

arXiv2022

QuFI: a Quantum Fault Injector to Measure the Reliability of Qubits and Quantum Circuits

Quantum computing is a new technology that is expected to revolutionize the computation paradigm in the next few years. Qubits exploit the quantum physics proprieties to increase the parallelism and speed of computation. Unfortunately, besides being intrinsically noisy, qubits have also been shown to be highly susceptible to external sources of faults, such as ionizing radiation. The latest discoveries highlight a much higher radiation sensitivity of qubits than traditional transistors and identify a much more complex fault model than bit-flip. We propose a framework to identify the quantum circuits sensitivity to radiation-induced faults and the probability for a fault in a qubit to propagate to the output. Based on the latest studies and radiation experiments performed on real quantum machines, we model the transient faults in a qubit as a phase shift with a parametrized magnitude. Additionally, our framework can inject multiple qubit faults, tuning the phase shift magnitude based on the proximity of the qubit to the particle strike location. As we show in the paper, the proposed fault injector is highly flexible, and it can be used on both quantum circuit simulators and real quantum machines. We report the finding of more than 285M injections on the Qiskit simulator and 53K injections on real IBM machines. We consider three quantum algorithms and identify the faults and qubits that are more likely to impact the output. We also consider the fault propagation dependence on the circuit scale, showing that the reliability profile for some quantum algorithms is scale-dependent, with increased impact from radiation-induced faults as we increase the number of qubits. Finally, we also consider multi qubits faults, showing that they are much more critical than single faults. The fault injector and the data presented in this paper are available in a public repository to allow further analysis.

cs.ET↗

arXiv2021

A Systematic Methodology to Compute the Quantum Vulnerability Factors for Quantum Circuits

Quantum computing is one of the most promising technology advances of the latest years. Once only a conceptual idea to solve physics simulations, quantum computation is today a reality, with numerous machines able to execute quantum algorithms. One of the hardest challenges in quantum computing is reliability. Qubits are highly sensitive to noise, which can make the output useless. Moreover, lately it has been shown that superconducting qubits are extremely susceptible to external sources of faults, such as ionizing radiation. When adopted in large scale, radiation-induced errors are expected to become a serious challenge for qubits reliability. In this paper, we propose an evaluation of the impact of transient faults in the execution of quantum circuits. Inspired by the Architectural and Program Vulnerability Factors, widely adopted to characterize the reliability of classical computing architectures and algorithms, we propose the Quantum Vulnerability Factor (QVF) as a metric to measure the impact that the corruption of a qubit has on the circuit output probability distribution. First, we model faults based on the latest studies on real machines and recently performed radiation experiments. Then, we design a quantum fault injector, built over Qiskit, and characterize the propagation of faults in quantum circuits. We report the finding of more than 15,000,000 fault injections, evaluating the reliability of three quantum circuits and identifying the faults and qubits that are more likely than others to impact the output. With our results, we give guidelines on how to map the qubits in the real quantum computer to reduce the output error and to reduce the probability of having a radiation-induced corruption to modify the output. Finally, we compare the simulation results with experiments on physical quantum computers.

cs.ET↗

arXiv2021

Experimental Findings on the Sources of Detected Unrecoverable Errors in GPUs

We investigate the sources of Detected Unrecoverable Errors (DUEs) in GPUs exposed to neutron beams. Illegal memory accesses and interface errors are among the more likely sources of DUEs. ECC increases the launch failure events. Our test procedure has shown that ECC can reduce the DUEs caused by Illegal Address access up to 92% for Kepler and 98% for Volta.

cs.DC↗

arXiv2020

Estimating Silent Data Corruption Rates Using a Two-Level Model

High-performance and safety-critical system architects must accurately evaluate the application-level silent data corruption (SDC) rates of processors to soft errors. Such an evaluation requires error propagation all the way from particle strikes on low-level state up to the program output. Existing approaches that rely on low-level simulations with fault injection cannot evaluate full applications because of their slow speeds, while application-level accelerated fault testing in accelerated particle beams is often impractical. We present a new two-level methodology for application resilience evaluation that overcomes these challenges. The proposed approach decomposes application failure rate estimation into (1) identifying how particle strikes in low-level unprotected state manifest at the architecture-level, and (2) measuring how such architecture-level manifestations propagate to the program output. We demonstrate the effectiveness of this approach on GPU architectures. We also show that using just one of the two steps can overestimate SDC rates and produce different trends---the composition of the two is needed for accurate reliability modeling.

cs.DC↗