Giuseppe Di Guglielmo
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View article: Sensor Co-design for $\textit{smartpixels}$
Sensor Co-design for $\textit{smartpixels}$ Open
Pixel tracking detectors at upcoming collider experiments will see unprecedented charged-particle densities. Real-time data reduction on the detector will enable higher granularity and faster readout, possibly enabling the use of the pixel…
View article: wa-hls4ml: A Benchmark and Surrogate Models for hls4ml Resource and Latency Estimation
wa-hls4ml: A Benchmark and Surrogate Models for hls4ml Resource and Latency Estimation Open
View article: Fast Machine Learning for Quantum Control of Microwave Qudits on Edge Hardware
Fast Machine Learning for Quantum Control of Microwave Qudits on Edge Hardware Open
Quantum optimal control is a promising approach to improve the accuracy of quantum gates, but it relies on complex algorithms to determine the best control settings. CPU or GPU-based approaches often have delays that are too long to be app…
View article: wa-hls4ml and lui-gnn: A benchmark and GNN-based surrogate model for hls4ml resource and latency estimation
wa-hls4ml and lui-gnn: A benchmark and GNN-based surrogate model for hls4ml resource and latency estimation Open
As machine learning (ML) increasingly serves as a tool for addressing real-time challenges in scientific applications, the development of advanced tooling has significantly reduced the time required to iterate on various designs. These adv…
View article: wa-hls4ml and lui-gnn: A Benchmark and GNN based Surrogate Model for hls4ml Resource and Latency Estimation
wa-hls4ml and lui-gnn: A Benchmark and GNN based Surrogate Model for hls4ml Resource and Latency Estimation Open
View article: End-to-end workflow for machine learning-based qubit readout with QICK and hls4ml
End-to-end workflow for machine learning-based qubit readout with QICK and hls4ml Open
We present an end-to-end workflow for superconducting qubit readout that embeds co-designed Neural Networks (NNs) into the Quantum Instrumentation Control Kit (QICK). Capitalizing on the custom firmware and software of the QICK platform, w…
View article: Sensor co-design for Smartpixels
Sensor co-design for Smartpixels Open
View article: End-to-End Workflow for Machine-Learning-Based Qubit Readout With QICK and hls4ml
End-to-End Workflow for Machine-Learning-Based Qubit Readout With QICK and hls4ml Open
In this article, we present an end-to-end workflow for superconducting qubit readout that embeds codesigned neural networks into the quantum instrumentation control kit (QICK). Capitalizing on the custom firmware and software of the QICK p…
View article: Intelligent pixel detectors: towards a radiation hard ASIC with on-chip machine learning in 28nm CMOS
Intelligent pixel detectors: towards a radiation hard ASIC with on-chip machine learning in 28nm CMOS Open
Detectors at future high energy colliders will face enormous technical challenges. Disentangling the unprecedented numbers of particles expected in each event will require highly granular silicon pixel detectors with billions of readout ch…
View article: Machine Learning for Arbitrary Single-Qubit Rotations on an Embedded Device
Machine Learning for Arbitrary Single-Qubit Rotations on an Embedded Device Open
Here we present a technique for using machine learning (ML) for single-qubit gate synthesis on field programmable logic for a superconducting transmon-based quantum computer based on simulated studies. Our approach is multi-stage. We first…
View article: Radiation-Hard Smart-Pixel Detector ASIC ReadOut with Digital AI in 28nm
Radiation-Hard Smart-Pixel Detector ASIC ReadOut with Digital AI in 28nm Open
Detectors at future high energy colliders will face enormous technical challenges. Disentangling the unprecedented numbers of particles expected in each event will require highly granular silicon pixel detectors with billions of readout ch…
View article: wa-hls4ml: A benchmark and dataset for ML accelerator resource estimation
wa-hls4ml: A benchmark and dataset for ML accelerator resource estimation Open
View article: Smart Pixels: In-pixel AI for on-sensor data filtering
Smart Pixels: In-pixel AI for on-sensor data filtering Open
We present a smart pixel prototype readout integrated circuit (ROIC) designed in CMOS 28 nm bulk process, with in-pixel implementation of an artificial intelligence (AI) / machine learning (ML) based data filtering algorithm designed as pr…
View article: Smart pixel sensors: towards on-sensor filtering of pixel clusters with deep learning
Smart pixel sensors: towards on-sensor filtering of pixel clusters with deep learning Open
Highly granular pixel detectors allow for increasingly precise measurements of charged particle tracks. Next-generation detectors require that pixel sizes will be further reduced, leading to unprecedented data rates exceeding those foresee…
View article: Low latency optical-based mode tracking with machine learning deployed on FPGAs on a tokamak
Low latency optical-based mode tracking with machine learning deployed on FPGAs on a tokamak Open
Active feedback control in magnetic confinement fusion devices is desirable to mitigate plasma instabilities and enable robust operation. Optical high-speed cameras provide a powerful, non-invasive diagnostic and can be suitable for these …
View article: Smart pixel sensors Towards on-sensor filtering of pixel clusters with deep learning
Smart pixel sensors Towards on-sensor filtering of pixel clusters with deep learning Open
High granularity silicon pixel sensors are at the heart of energy frontier particle physics collider experiments. At an collision rate of 40\\,MHz, these detectors create massive amounts of data. Signal processing that handles data incomin…
View article: Smartpixels: Towards on-sensor inference of charged particle track parameters and uncertainties
Smartpixels: Towards on-sensor inference of charged particle track parameters and uncertainties Open
The combinatorics of track seeding has long been a computational bottleneck for triggering and offline computing in High Energy Physics (HEP), and remains so for the HL-LHC. Next-generation pixel sensors will be sufficiently fine-grained t…
View article: Smart Pixels: towards on-sensor inference of charged particle track parameters and uncertainties
Smart Pixels: towards on-sensor inference of charged particle track parameters and uncertainties Open
The combinatorics of track seeding has long been a computational bottleneck for triggering and offline computing in High Energy Physics (HEP), and remains so for the HL-LHC. Next-generation pixel sensors will be sufficiently fine-grained t…
View article: Low latency optical-based mode tracking with machine learning deployed on FPGAs on a tokamak
Low latency optical-based mode tracking with machine learning deployed on FPGAs on a tokamak Open
Active feedback control in magnetic confinement fusion devices is desirable to mitigate plasma instabilities and enable robust operation. Optical high-speed cameras provide a powerful, non-invasive diagnostic and can be suitable for these …
View article: Corrigendum: Applications and techniques for fast machine learning in science
Corrigendum: Applications and techniques for fast machine learning in science Open
[This corrects the article DOI: 10.3389/fdata.2022.787421.].
View article: Smart pixel sensors: towards on-sensor filtering of pixel clusters with deep learning
Smart pixel sensors: towards on-sensor filtering of pixel clusters with deep learning Open
Highly granular pixel detectors allow for increasingly precise measurements of charged particle tracks. Next-generation detectors require that pixel sizes will be further reduced, leading to unprecedented data rates exceeding those foresee…
View article: In-pixel AI for lossy data compression at source for X-ray detectors
In-pixel AI for lossy data compression at source for X-ray detectors Open
View article: Implementing machine learning methods on QICK hardware for qubit readout & control
Implementing machine learning methods on QICK hardware for qubit readout & control Open
Quantum readout and control is a fundamental aspect of quantum computing that requires accurate measurement of qubit states. Errors emerge in all stages, from initialization to readout, and identifying errors in post-processing necessitate…
View article: Neural network accelerator for quantum control
Neural network accelerator for quantum control Open
Technology (FAST) facility's low energy beamline using simulated virtual cathode laser images, gun phases, and solenoid strengths.
View article: Neural network accelerator for quantum control
Neural network accelerator for quantum control Open
Efficient quantum control is necessary for practical quantum computing\nimplementations with current technologies. Conventional algorithms for\ndetermining optimal control parameters are computationally expensive, largely\nexcluding them f…
View article: Open-source FPGA-ML codesign for the MLPerf Tiny Benchmark
Open-source FPGA-ML codesign for the MLPerf Tiny Benchmark Open
We present our development experience and recent results for the MLPerf Tiny Inference Benchmark on field-programmable gate array (FPGA) platforms. We use the open-source hls4ml and FINN workflows, which aim to democratize AI-hardware code…
View article: Real-Time Inference With 2D Convolutional Neural Networks on Field Programmable Gate Arrays for High-Rate Particle Imaging Detectors
Real-Time Inference With 2D Convolutional Neural Networks on Field Programmable Gate Arrays for High-Rate Particle Imaging Detectors Open
We present a custom implementation of a 2D Convolutional Neural Network (CNN) as a viable application for real-time data selection in high-resolution and high-rate particle imaging detectors, making use of hardware acceleration in high-end…
View article: Compressing deep neural networks on FPGAs to binary and ternary precision with HLS4ML
Compressing deep neural networks on FPGAs to binary and ternary precision with HLS4ML Open
We present the implementation of binary and ternary neural networks in the hls4ml library, designed to automatically convert deep neural network models to digital circuits with FPGA firmware. Starting from benchmark models trained with flo…
View article: Applications and Techniques for Fast Machine Learning in Science
Applications and Techniques for Fast Machine Learning in Science Open
In this community review report, we discuss applications and techniques for fast machine learning (ML) in science—the concept of integrating powerful ML methods into the real-time experimental data processing loop to accelerate scientific …
View article: Real-time Inference with 2D Convolutional Neural Networks on Field Programmable Gate Arrays for High-rate Particle Imaging Detectors
Real-time Inference with 2D Convolutional Neural Networks on Field Programmable Gate Arrays for High-rate Particle Imaging Detectors Open
We present a custom implementation of a 2D Convolutional Neural Network (CNN) as a viable application for real-time data selection in high-resolution and high-rate particle imaging detectors, making use of hardware acceleration in high-end…