arXiv subjects

Fabrizio Riente

Publications and source records attributed to Fabrizio Riente.

7 recordsLinked to original sources

arXiv2026

FIREQ: FPGA Instrumentation for Readout and Qubit control

We present FIREQ (FPGA Instrumentation for Readout and Qubit control), an open-source RFSoC-based framework for the control and readout of superconducting qubits. FIREQ combines a modular AXI-compliant firmware architecture with a PYNQ-based software stack designed to support extensible hardware integration, deterministic experiment timing, and low-overhead execution of repeated calibration and characterization workflows. The firmware implements direct RF synthesis and acquisition, trigger-based sequencing, programmable pulse generation, frequency-multiplexed readout, and memory-efficient acquisition and waveform buffering. The software adopts a client-server architecture with streamed data transfer and dependency-aware configuration updates to reduce host-device and reconfiguration overhead during parameter sweeps. On an AMD Zynq UltraScale+ RFSoC ZCU216, FIREQ generates RF pulses up to 9.3 GHz with a pulse-duration resolution of 107 ps and an event-timing resolution of 1.7 ns. FPGA resource utilization is compared with representative open-source RFSoC control frameworks, showing a low BRAM footprint while retaining full-rate I/Q generation and acquisition. The RF output is characterized in terms of phase noise, noise spectral density, and inter-channel timing skew. End-to-end operation is validated on a superconducting qubit through resonator spectroscopy, Rabi, Ramsey, and relaxation measurements, yielding T1 = 6.94 us and T2* = 13.50 us. FIREQ can therefore be used both as a qubit-control platform and as an experimental environment for evaluating alternative control and readout IP architectures.

quant-ph↗

arXiv2026

DynamicSound simulator for simulating moving sources and microphone arrays

Developing algorithms for sound classification, detection, and localization requires large amounts of flexible and realistic audio data, especially when leveraging modern machine learning and beamforming techniques. However, most existing acoustic simulators are tailored for indoor environments and are limited to static sound sources, making them unsuitable for scenarios involving moving sources, moving microphones, or long-distance propagation. This paper presents DynamicSound an open-source acoustic simulation framework for generating multichannel audio from one or more sound sources with the possibility to move them continuously in three-dimensional space and recorded by arbitrarily configured microphone arrays. The proposed model explicitly accounts for finite sound propagation delays, Doppler effects, distance-dependent attenuation, air absorption, and first-order reflections from planar surfaces, yielding temporally consistent spatial audio signals. Unlike conventional mono or stereo simulators, the proposed system synthesizes audio for an arbitrary number of virtual microphones, accurately reproducing inter-microphone time delays, level differences, and spectral coloration induced by the environment. Comparative evaluations with existing open-source tools demonstrate that the generated signals preserve high spatial fidelity across varying source positions and acoustic conditions. By enabling the generation of realistic multichannel audio under controlled and repeatable conditions, the proposed open framework provides a flexible and reproducible tool for the development, training, and evaluation of modern spatial audio and sound-source localization algorithms.

eess.AS↗

arXiv2025

Optimizing Uncertainty-Aware Deep Learning for On-the-Edge Murmur Detection in Low-Resource Settings

Early and reliable detection of heart murmurs is essential for the timely diagnosis of cardiovascular diseases, yet traditional auscultation remains subjective and dependent on expert interpretation. This work investigates artificial intelligence (AI)-based murmur detection using the CirCor Heart Sound dataset, with a focus on enabling uncertainty-aware, resource-efficient deployment on edge devices. Three convolutional neural network (CNN) architectures of increasing complexity (Light, Baseline, and Heavy) were compared in terms of classification performance, computational cost, and suitability for on-device inference. Additionally, Monte Carlo Dropout was applied for uncertainty estimation, providing confidence measures to improve prediction sensitivity. Results show that lightweight models can achieve accuracy comparable to deeper networks (91%) while requiring two orders of magnitude fewer parameters. Incorporating uncertainty-based selective classification further improved sensitivity by 3%, enhancing robustness and clinical reliability. The findings highlight the feasibility of developing computationally efficient, uncertainty-aware AI systems for heart murmur screening in low-resource and remote healthcare settings.

eess.SP↗

arXiv2025

High-Parallel FPGA-Based Discrete Simulated Bifurcation for Large-Scale Optimization

Combinatorial Optimization (CO) problems exhibit exponential complexity, making their resolution challenging. Simulated Adiabatic Bifurcation (aSB) is a quantum-inspired algorithm to obtain approximate solutions to largescale CO problems written in the Ising form. It explores the solution space by emulating the adiabatic evolution of a network of Kerr-nonlinear parametric oscillators (KPOs), where each oscillator represents a variable in the problem. The optimal solution corresponds to the ground state of this system. A key advantage of this approach is the possibility of updating multiple variables simultaneously, making it particularly suited for hardware implementation. To enhance solution quality and convergence speed, variations of the algorithm have been proposed in the literature, including ballistic (bSB), discrete (dSB), and thermal (HbSB) versions. In this work, we have comprehensively analyzed dSB, bSB, and HbSB using dedicated software models, evaluating the feasibility of using a fixed-point representation for hardware implementation. We then present an opensource hardware architecture implementing the dSB algorithm for Field-Programmable Gate Arrays (FPGAs). The design allows users to adjust the degree of algorithmic parallelization based on their specific requirements. A proof-of-concept implementation that solves 256-variable problems was achieved on an AMD Kria KV260 SoM, a low-tier FPGA, validated using well-known max-cut and knapsack problems.

eess.SY↗

arXiv2025

Incremental Firmware Update Over-the-Air for Low-Power IoT Devices over LoRaWAN

Efficiently supporting remote firmware updates in Internet of Things (IoT) devices remains a significant challenge due to the limitations of many IoT communication protocols, which often make it impractical to transmit full firmware images. Techniques such as firmware partitioning have been introduced to mitigate this issue, but they frequently fall short, especially in battery-powered systems where time and energy constraints are critical. As a result, physical maintenance interventions are still commonly required, which is costly and inconvenient in large-scale deployments. In this work, we present a lightweight and innovative method that addresses this challenge by generating highly compact delta patches, enabling firmware reconstruction directly on the device. Our algorithm is specifically optimized for low-power devices, minimizing both memory usage and computational overhead. Compared to existing solutions, our approach significantly reduces the data volume needed for updates while maintaining performance comparable to more complex alternatives. Experimental evaluations confirm that our method yields substantial time and energy savings, making it particularly well-suited for battery-powered IoT nodes. Although our implementation targets the LoRaWAN protocol, the approach is flexible and can be adapted to other IoT communication technologies.

eess.SY↗

arXiv2023

Simulation Environment with Customized RISC-V Instructions for Logic-in-Memory Architectures

Nowadays, various memory-hungry applications like machine learning algorithms are knocking "the memory wall". Toward this, emerging memories featuring computational capacity are foreseen as a promising solution that performs data process inside the memory itself, so-called computation-in-memory, while eliminating the need for costly data movement. Recent research shows that utilizing the custom extension of RISC-V instruction set architecture to support computation-in-memory operations is effective. To evaluate the applicability of such methods further, this work enhances the standard GNU binary utilities to generate RISC-V executables with Logic-in-Memory (LiM) operations and develop a new gem5 simulation environment, which simulates the entire system (CPU, peripherals, etc.) in a cycle-accurate manner together with a user-defined LiM module integrated into the system. This work provides a modular testbed for the research community to evaluate potential LiM solutions and co-designs between hardware and software.

cs.AR↗

arXiv2021

Controlling Domain-Wall Nucleation in Ta/CoFeB/MgO Nanomagnets via Local Ga+ Ion Irradiation

Comprehensive control of the domain wall nucleation process is crucial for spin-based emerging technologies ranging from random-access and storage-class memories over domain-wall logic concepts to nanomagnetic logic. In this work, focused Ga+ ion-irradiation is investigated as an effective means to control domain-wall nucleation in Ta/CoFeB/MgO nanostructures. We show that analogously to He+ irradiation, it is not only possible to reduce the perpendicular magnetic anisotropy but also to increase it significantly, enabling new, bidirectional manipulation schemes. First, the irradiation effects are assessed on film level, sketching an overview of the dose-dependent changes in the magnetic energy landscape. Subsequent time-domain nucleation characteristics of irradiated nanostructures reveal substantial increases in the anisotropy fields but surprisingly small effects on the measured energy barriers, indicating shrinking nucleation volumes. Spatial control of the domain wall nucleation point is achieved by employing focused irradiation of pre-irradiated magnets, with the diameter of the introduced circular defect controlling the coercivity. Special attention is given to the nucleation mechanisms, changing from a Stoner-Wohlfarth particle's coherent rotation to depinning from an anisotropy gradient. Dynamic micromagnetic simulations and related measurements are used in addition to model and analyze this depinning-dominated magnetization reversal.

cond-mat.mes-hall↗