Related papers: Real-Time Data Processing in the Muon System of th…

The DISTO collaboration has built a large-acceptance magnetic spectrometer designed to provide broad kinematic coverage of multi-particle final states produced in $pp$ scattering. The spectrometer has been installed in the polarized proton…

This paper describes the mechanical design, the readout chain, the production, testing and the installation of the Silicon Microstrip Tracker of the D0 experiment at the Fermilab Tevatron collider. In addition, description of the…

LiDAR sensors have been widely used in many autonomous vehicle modalities, such as perception, mapping, and localization. This paper presents an FPGA-based deep learning platform for real-time point cloud processing targeted on autonomous…

The muon campus program at Fermilab includes the Mu2e experiment that will search for a charged-lepton flavor violating processes where a negative muon converts into an electron in the field of an aluminum nucleus, improving by four orders…

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 CDF Run II silicon detector is the largest operating detector of its kind in High Energy Physics, collecting p-pbar collision data at the Fermilab Tevatron since 2001. It provides precision tracking and vertexing which played a critical…

We report on the development of a front-end ASIC for silicon-strip detectors of the J-PARC Muon g-2/EDM experiment. This experiment aims to measure the muon anomalous magnetic moment and electric dipole moment precisely to explore new…

The installation of the muon telescope detector opened new possibilities for studying dimuon production at STAR. However, backgrounds from hadron punch-through and weak decays of pions and kaons make the identification of primary muons…

Time-domain nonvolatile in-memory computing (TD-nvIMC) offers a promising pathway to reduce data movement and improve energy efficiency by encoding computation in delay rather than voltage or current. This work presents a fully integrated…

Continual learning on edge platforms remains challenging because recurrent networks depend on energy-intensive training procedures and frequent data movement that are impractical for embedded deployments. This work introduces M2RU, a…

In order to solve the problems of waveform distortion and signal delay by many physical and electrical systems with multi-pole linear low-pass transfer characteristics, a simple digital-signal-processing (DSP)-based method of real-time…

Experiments like ATLAS at the HL-LHC or detectors at future hadron colliders need muon detectors with excellent momentum resolution up to the TeV scale both at the trigger and offline reconstruction levels. This requires muon tracking…

We propose a fully asynchronous peak detection approach for SPAD-based direct time-of-flight (dToF) flash LiDAR, enabling pixel-wise event-driven depth acquisition without global synchronization. By allowing pixels to independently report…

The Compressed Baryonic Matter~(CBM) experiment in the upcoming Facility for Antiproton and Ion Research~(FAIR), designed to take data in nuclear collisions at very high interaction rates of up to 10 MHz, will employ a free-streaming data…

The increasing adoption of Deep Neural Network (DNN)-based Digital Pre-distortion (DPD) in modern communication systems necessitates efficient hardware implementations. This paper presents DPD-NeuralEngine, an ultra-fast, tiny-area, and…

A new Muon-to-Central-Trigger Processor Interface (MUCTPI) was built as part of the upgrade of the ATLAS Level-1 trigger system for the next Run of the Large Hadron Collider at CERN. The MUCTPI has 208 high-speed optical serial links for…

The need to execute Deep Neural Networks (DNNs) at low latency and low power at the edge has spurred the development of new heterogeneous Systems-on-Chips (SoCs) encapsulating a diverse set of hardware accelerators. How to optimally map a…

As a test of general applicability, we use the recently proposed spin-wave delay line active-ring reservoir computer to perform the spoken digit recognition task. On this, classification accuracies of up to 93% are achieved. The tested…

Accurate and low-latency qubit state measurement is critical for trapped-ion quantum computing. While deep neural networks (DNNs) have been integrated to enhance detection fidelity, their latency performance on specific hardware platforms…