arXiv subjects

Stefano Chiappini

Publications and source records attributed to Stefano Chiappini.

7 recordsLinked to original sources

arXiv2026

A general framework for disturbance compensation in airborne and seaborne gravimetry supported by machine learning

High-accuracy measurement systems operating in complex operational environments, such as airborne and seaborne gravimetry, are severely affected by interacting external influences (disturbances) including temperature variations, inertial accelerations, and platform rotations. This work aims to develop and prove a general method for compensating such disturbances beyond the limits of conventional approaches. We present a general framework based on three pillars: a multi-sensor system, supervised machine learning, and a dedicated laboratory -training platform-. This measurement technique was investigated through two experimental case studies relevant to airborne and seaborne gravimetry: (i) temperature and thermal-gradient rejection in a high-sensitivity tri-axial accelerometer, and (ii) pitch and roll rejection for vertical acceleration estimation. The experiments highlighted that, although the general framework provides a useful guideline, its application to airborne and seaborne gravimetry is challenging and not straightforward, requiring the development of dedicated and innovative experimental solutions. This difficulty arises from the specific nature of the measurand-gravity-which cannot be easily varied under controlled conditions. In this work, we present a dedicated approach to addressing these challenges.

physics.ins-det↗

arXiv2026

A scalable and resource-efficient pipelined p-computer for probabilistic Ising machines

Probabilistic Ising machines (PIMs) based on probabilistic bits offer a hardware-friendly route to solve combinatorial optimization problems, but most digital implementations achieve high throughput by exploiting sparse interactions. This limits their applicability to dense problems, for which memory bandwidth and data movement become the dominant bottlenecks. Here, we show a resource-efficient pipelined Field-Programmable Gate Array architecture enabling high-throughput execution of fully-connected PIMs while maintaining scalability and modularity. This architecture design combines a deeply pipelined (>20 stages) probabilistic bit update path, which overlaps spin evaluation and local-field updates, with a bandwidth-aware on-chip memory organization for the coupling and bias matrices. The architecture supports 512 p-bits with 16-bit fixed-point coefficients and 1024 and 2048 p-bits with 10-bit and 2-bit coefficients, respectively, and operates at up to 300 MHz. At fixed degree of parallelization, it delivers an order-of-magnitude higher update rate than an optimized non-pipelined baseline, while improving the time-area trade-off for dense workloads. Validation on portfolio optimization and low-density parity-check decoding shows close agreement with software references and substantial reductions in time-to-solution relative to the non-pipelined design, establishing pipelining as an effective route to scalable digital probabilistic computing for dense optimization problems.

eess.SY↗

arXiv2025

High-performance and reliable probabilistic Ising machine based on simulated quantum annealing

Probabilistic computing with pbits is emerging as a computational paradigm for machine learning and for facing combinatorial optimization problems (COPs) with the so-called probabilistic Ising machines (PIMs). From a hardware point of view, the key elements that characterize a PIM are the random number generation, the nonlinearity, the network of coupled pbits, and the energy minimization algorithm. Regarding the latter, in this work we show that PIMs using the simulated quantum annealing (SQA) schedule exhibit better performance as compared to simulated annealing and parallel tempering in solving a number of COPs, such as maximum satisfiability problems, planted Ising problem, and travelling salesman problem. Additionally, we design and simulate the architecture of a fully connected CMOS based PIM able to run the SQA algorithm having a spin-update time of 8 ns with a power consumption of 0.22 mW. Our results also show that SQA increases the reliability and the scalability of PIMs by compensating for device variability at an algorithmic level enabling the development of their implementation combining CMOS with different technologies such as spintronics. This work shows that the characteristics of the SQA are hardware agnostic and can be applied in the co-design of any hybrid analog digital Ising machine implementation. Our results open a promising direction for the implementation of a new generation of reliable and scalable PIMs.

cond-mat.mes-hall↗

arXiv2024

Temperature compensation in high accuracy accelerometers using multi-sensor and machine learning methods

Temperature is a major source of inaccuracy in high-sensitivity accelerometers and gravimeters. Active thermal control systems require power and may not be ideal in some contexts such as airborne or spaceborne applications. We propose a solution that relies on multiple thermometers placed within the accelerometer to measure temperature and thermal gradient variations. Machine Learning algorithms are used to relate the temperatures to their effect on the accelerometer readings. However, obtaining labeled data for training these algorithms can be difficult. Therefore, we also developed a training platform capable of replicating temperature variations in a laboratory setting. Our experiments revealed that thermal gradients had a significant effect on accelerometer readings, emphasizing the importance of multiple thermometers. The proposed method was experimentally tested and revealed a great potential to be extended to other sources of inaccuracy, such as rotations, as well as to other types of measuring systems, such as magnetometers or gyroscopes.

physics.ins-det↗

arXiv2022

Spintronics-compatible approach to solving maximum satisfiability problems with probabilistic computing, invertible logic and parallel tempering

The search of hardware-compatible strategies for solving NP-hard combinatorial optimization problems (COPs) is an important challenge of today s computing research because of their wide range of applications in real world optimization problems. Here, we introduce an unconventional scalable approach to face maximum satisfiability problems (Max-SAT) which combines probabilistic computing with p-bits, parallel tempering, and the concept of invertible logic gates. We theoretically show the spintronic implementation of this approach based on a coupled set of Landau-Lifshitz-Gilbert equations, showing a potential path for energy efficient and very fast (p-bits exhibiting ns time scale switching) architecture for the solution of COPs. The algorithm is benchmarked with hard Max-SAT instances from the 2016 Max-SAT competition (e.g., HG-4SAT-V150-C1350-1.cnf which can be described with 2851 p-bits), including weighted Max-SAT and Max-Cut problems.

cond-mat.mes-hall↗

arXiv2021

Magnetization Reversal Signatures of Hybrid and Pure Néel Skyrmions in Thin Film Multilayers

We report a study of the magnetization reversals and skyrmion configurations in two systems - Pt/Co/MgO and Ir/Fe/Co/Pt multilayers, where magnetic skyrmions are stabilized by a combination of dipolar and Dzyaloshinskii-Moriya interactions (DMI). First Order Reversal Curve (FORC) diagrams of low-DMI Pt/Co/MgO and high-DMI Ir/Fe/Co/Pt exhibit stark differences, which are identified by micromagnetic simulations to be indicative of hybrid and pure Néel skyrmions, respectively. Tracking the evolution of FORC features in multilayers with dipolar interactions and DMI, we find that the negative FORC valley, typically accompanying the positive FORC peak near saturation, disappears under both reduced dipolar interactions and enhanced DMI. As these conditions favor the formation of pure Neel skyrmions, we propose that the resultant FORC feature - a single positive FORC peak near saturation - can act as a fingerprint for pure Néel skyrmions in multilayers. Our study thus expands on the utility of FORC analysis as a tool for characterizing spin topology in multilayer thin films.

cond-mat.mes-hall↗

arXiv2018

Micromagnetic understanding of the skyrmion Hall angle current dependence in perpendicular magnetized ferromagnets

The understanding of the dynamical properties of skyrmion is a fundamental aspect for the realization of a competitive skyrmion based technology beyond CMOS. Most of the theoretical approaches are based on the approximation of a rigid skyrmion. However, thermal fluctuations can drive a continuous change of the skyrmion size via the excitation of thermal modes. Here, by taking advantage of the Hilbert-Huang transform, we demonstrate that at least two thermal modes can be excited which are non-stationary in time. In addition, one limit of the rigid skyrmion approximation is that this hypothesis does not allow for correctly describing the recent experimental evidence of skyrmion Hall angle dependence on the amplitude of the driving force, which is proportional to the injected current. In this work, we show that, in an ideal sample, the combined effect of field-like and damping-like torques on a breathing skyrmion can indeed give rise to such a current dependent skyrmion Hall angle. While here we design and control the breathing mode of the skyrmion, our results can be linked to the experiments by considering that the thermal fluctuations and/or disorder can excite the breathing mode. We also propose an experiment to validate our findings.

cond-mat.mes-hall↗