Related papers: On-chip probabilistic inference for charged-partic…
The development of n-on-p "edgeless" planar pixel sensors being fabricated at FBK (Trento, Italy), aimed at the upgrade of the ATLAS Inner Detector for the High Luminosity phase of the Large Hadron Collider (HL-LHC), is reported. A…
In recent years, machine learning has developed rapidly, enabling the development of applications with high levels of recognition accuracy relating to the use of speech and images. However, other types of data to which these models can be…
The success of deep neural networks (DNNs) is heavily dependent on computational resources. While DNNs are often employed on cloud servers, there is a growing need to operate DNNs on edge devices. Edge devices are typically limited in their…
The two innermost layers of the ALICE inner tracking system are instrumented with silicon pixel detectors. Single chip assembly prototypes of the ALICE pixels have been tested in high energy particle beams at the CERN SPS. Detection…
We develop, discuss, and compare several inference techniques to constrain theory parameters in collider experiments. By harnessing the latent-space structure of particle physics processes, we extract extra information from the simulator.…
For its physics program with a high-intensity hadron beam of up to 2e7 particles/s, the COMPASS experiment at CERN requires tracking of charged particles scattered by very small angles with respect to the incident beam direction. While good…
To cope with the High Luminosity LHC harsh conditions, the ATLAS inner tracker has to be upgraded to meet requirements in terms of radiation hardness, pile up and geometrical acceptance. The active edge technology allows to reduce the…
Edge inference has become more widespread, as its diverse applications range from retail to wearable technology. Clusters of networked resource-constrained edge devices are becoming common, yet no system exists to split a DNN across these…
As machine learning inferences increasingly move to edge devices, adapting to diverse computational capabilities, hardware, and memory constraints becomes more critical. Instead of relying on a pre-trained model fixed for all future…
The past ten years have seen the advent of silicon-based precise timing detectors for charged particle tracking. The underlying reason for this evolution is a design innovation: the Low-Gain Avalanche Diode (LGAD). In its simplicity, the…
Ensembles of deep neural networks demonstrate improved performance over single models. For enhancing the diversity of ensemble members while keeping their performance, particle-based inference methods offer a promising approach from a…
Photonic integrated circuits (PICs) have emerged as a scalable platform for complex quantum technologies using photonic and atomic systems. A central goal has been to integrate photon-resolving detectors to reduce optical losses, latency,…
Edges of an image are considered a crucial type of information. These can be extracted by applying edge detectors with different methodology. Edge detection is a vital step in computer vision tasks, because it is an essential issue for…
We develop and study FPGA implementations of algorithms for charged particle tracking based on graph neural networks. The two complementary FPGA designs are based on OpenCL, a framework for writing programs that execute across heterogeneous…
Experimental particle physics demands a sophisticated trigger and acquisition system capable to efficiently retain the collisions of interest for further investigation. Heterogeneous computing with the employment of FPGA cards may emerge as…
Recent progress in active-edge technology of silicon sensors enables the development of large-area tiled silicon pixel detectors with small dead space between modules by utilizing edgeless sensors. Such technology has been proven in…
The advent of the Edge Computing (EC) leads to a huge ecosystem where numerous nodes can interact with data collection devices located close to end users. Human detection and tracking can be realized at edge nodes that perform the…
The High Luminosity upgrade of the CERN Large Hadron Collider will be able to reach a peak instantaneous luminosity of 5E34/cm2 s. The innermost detectors of the CMS and ATLAS experiments will have to cope with unprecedented requirements on…
Task-oriented integrated sensing, communication, and computation (ISCC) is a key technology for achieving low-latency edge inference and enabling efficient implementation of artificial intelligence (AI) in industrial cyber-physical systems…
While modern machine learning has transformed numerous application domains, its growing computational demands increasingly constrain scalability and efficiency, particularly on embedded and resource-limited platforms. In practice, neural…