相关论文: Development of Deep Neural Network First-Level Har…
The upgrades of the Belle experiment and the KEKB accelerator aim to increase the data set of the experiment by the factor 50. This will be achieved by increasing the luminosity of the accelerator which requires a significant upgrade of the…
After 2001 the upgraded ep collider HERA will provide an about five times higher luminosity for the two experiments H1 and ZEUS. In order to cope with the expected higher event rates the H1 collaboration is building a track based trigger…
Since the start of data taking in spring 2019 at the SuperKEKB collider (KEK, Japan) the Belle II Silicon Vertex Detector (SVD) has been operating reliably and with high efficiency, while providing high quality data: high signal-to-noise…
The Belle II detector at the SuperKEKB accelerator complex is covering a wide range of exciting physics topics. To achieve the project's research goals, a substantial increase of the data sample to 50~ab$^{-1}$ is needed, and for that, the…
Brain-inspired spiking neural networks (SNNs) replace the multiply-accumulate operations of traditional neural networks by integrate-and-fire neurons, with the goal of achieving greater energy efficiency. Specialized hardware…
Deep neural networks (DNNs) have the advantage that they can take into account a large number of parameters, which enables them to solve complex tasks. In computer vision and speech recognition, they have a better accuracy than common…
Graph neural networks are increasingly adopted in trigger systems for collider experiments, where strict latency and throughput constraints render deployment on embedded platforms challenging. As detectors move towards higher granularity,…
The silicon vertex detector (SVD) is installed at the heart of the Belle II experiment, taking data at the high-luminosity $B$-Factory SuperKEKB since 2019. The detector has shown a stable and above-99\% hit efficiency, with a large…
In 2019 the Belle II experiment started data taking at the asymmetric SuperKEKB collider (KEK, Japan) operating at the Y(4S) resonance. Belle II will search for new physics beyond the Standard Model by collecting an integrated luminosity of…
The current work reports an efficient deep neural network (DNN) accelerator where synaptic weight elements are controlled by ferroelectric domain dynamics. An integrated device-to-algorithm framework for benchmarking novel synaptic devices…
We present a design methodology that enables the semi-automatic generation of a hardware-accelerated graph building architectures for locally constrained graphs based on formally described detector definitions. In addition, we define a…
In 2024, the Belle II experiment resumed data taking after the Long Shutdown 1, which was required to install a two-layer pixel detector and upgrade accelerator components. We describe the challenges of this shutdown and the operational…
High-energy physics experiments require fast and efficient methods for reconstructing the tracks of charged particles. The commonly used algorithms are sequential, and the required CPU power increases rapidly with the number of tracks.…
The SuperKEKB accelerator in Tsukuba, Japan is providing e$^+$e$^-$ beams for the Belle II experiment since March 2019. To deal with the aimed peak luminosity being forty times higher than the one recorded at Belle, a pixel detector based…
The new Belle II experiment at the asymmetric $e^+ e^-$ accelerator SuperKEKB at KEK in Japan is designed to deliver a peak luminosity of $8\times10^{35}\text{cm}^{-2}\text{s}^{-1}$. To perform high-precision track reconstruction, e.g. for…
Muon identification is crucial for elementary particle physics experiments. At the Belle II experiment, muons and pions with momenta greater than 0.7 GeV/c are distinguished by their penetration ability through the $K_L$ and Muon (KLM)…
Hardware accelerations of deep learning systems have been extensively investigated in industry and academia. The aim of this paper is to achieve ultra-high energy efficiency and performance for hardware implementations of deep neural…
The record-breaking achievements of deep neural networks (DNNs) in image classification and detection tasks resulted in a surge of new computer vision applications during the past years. However, their computational complexity is…
We briefly present the eXtremely Fast Tracker stereo track upgrade for the CDF Level 2 trigger system. This upgrade enabled full 3D track reconstruction at Level 2 of the 3-Level CDF online triggering system. Using information provided by…
In hadron collider experiments, triggering the detector to store interesting events for offline analysis is a challenge due to the high rates and multiplicities of particles produced. Maintaining high trigger efficiency for the physics we…