Tootfinder

High Precision RF Pulse Shaping with Direct RF Sampling for Future Linear Accelerators
Chao Liu, Ankur Dhar, Ryan Herbst, Emilio A. Nanni
https://arxiv.org/abs/2505.22872

High Precision RF Pulse Shaping with Direct RF Sampling for Future Linear Accelerators
In various of particle accelerator designs, amplitude and phase modulation methods are commonly applied to shape the RF pulses for implementing pulse compressors or compensating for the fluctuations introduced by the high-power RF components and beam loading effects. Phase modulations are typically implemented with additional phase shifters that require drive or control electronics. With our recent next-generation LLRF (NG-LLRF) platform developed based on direct RF sampling technology of RF sy…

SpeedLLM: An FPGA Co-design of Large Language Model Inference Accelerator
Peipei Wang, Wu Guan, Liping Liang, Zhijun Wang, Hanqing Luo, Zhibin Zhang
https://arxiv.org/abs/2507.14139

SpeedLLM: An FPGA Co-design of Large Language Model Inference Accelerator
This paper introduces SpeedLLM, a neural network accelerator designed on the Xilinx Alevo U280 platform and optimized for the Tinyllama framework to enhance edge computing performance. Key innovations include data stream parallelism, a memory reuse strategy, and Llama2 operator fusion, which collectively reduce latency and energy consumption. SpeedLLM's data pipeline architecture optimizes the read-compute-write cycle, while the memory strategy minimizes FPGA resource demands. The operator fusi…

Tuning chiral anomaly signature in a Dirac semimetal via fast-ion implantation
Manasi Mandal, Eunbi Rha, Abhijatmedhi Chotrattanapituk, Denisse C\'ordova Carrizales, Alexander Lygo, Kevin B. Woller, Mouyang Cheng, Ryotaro Okabe, Guomin Zhu, Kiran Mak, Chu-Liang Fu, Chuhang Liu, Lijun Wu, Yimei Zhu, Susanne Stemmer, Mingda Li
https://ar…

Tuning chiral anomaly signature in a Dirac semimetal via fast-ion implantation
Cd$_3$As$_2$ is a prototypical Dirac semimetal that hosts a chiral anomaly and thereby functions as a platform to test high-energy physics hypotheses and to realize energy efficient applications. Here we use a combination of accelerator-based fast ion implantation and theory-driven planning to enhance the negative longitudinal magnetoresistance (NLMR)--a signature of a chiral anomaly--in Nb-doped Cd$_3$As$_2$ thin films. High-energy ion implantation is commonly used to investigate semiconductor…

Multi-messenger dynamic imaging of laser-driven shocks in water using a plasma wakefield accelerator
Mario D. Balcazar, Hai-En Tsai, Tobias Ostermayr, Paul T. Campbell, Qiang Chen, Cary Colgan, Gillis M. Dyer, Zachary Eisentraut, Eric Esarey, Cameron G. R. Geddes, Benjamin Greenwood, Anthony Gonsalves, Sahel Hakimi, Robert Jacob, Brendan Kettle, Paul King, Karl Krushelnick, Nuno Lemos, Eva Los, Yong Ma, Stuart P. D. Mangles, John Nees, Isabella M. Pagano, Carl Schroeder, Raspberry Simp…

Multi-messenger dynamic imaging of laser-driven shocks in water using a plasma wakefield accelerator
Understanding dense matter hydrodynamics is critical for predicting plasma behavior in environments relevant to laser-driven inertial confinement fusion. Traditional diagnostic sources face limitations in brightness, spatiotemporal resolution, and inability to detect relevant electromagnetic fields. In this work, we present a dual-probe, multi-messenger laser wakefield accelerator platform combining ultrafast X-rays and relativistic electron beams at 1 Hz, to interrogate a free-flowing water ta…

Serving LLMs in HPC Clusters: A Comparative Study of Qualcomm Cloud AI 100 Ultra and High-Performance GPUs
Mohammad Firas Sada, John J. Graham, Elham E Khoda, Mahidhar Tatineni, Dmitry Mishin, Rajesh K. Gupta, Rick Wagner, Larry Smarr, Thomas A. DeFanti, Frank W\"urthwein
https://arxiv.org/abs/2507.00418

Serving LLMs in HPC Clusters: A Comparative Study of Qualcomm Cloud AI 100 Ultra and High-Performance GPUs
This study presents a benchmarking analysis of the Qualcomm Cloud AI 100 Ultra (QAic) accelerator for large language model (LLM) inference, evaluating its energy efficiency (throughput per watt) and performance against leading NVIDIA (A100, H200) and AMD (MI300A) GPUs within the National Research Platform (NRP) ecosystem. A total of 15 open-source LLMs, ranging from 117 million to 90 billion parameters, are served using the vLLM framework. The QAic inference cards appears to be energy efficient…

Inverse-designed nanophotonic neural network accelerators for ultra-compact optical computing
Joel Sved, Shijie Song, Liwei Li, George Li, Debin Meng, Xiaoke Yi
https://arxiv.org/abs/2506.06150

Inverse-designed nanophotonic neural network accelerators for ultra-compact optical computing
Inverse-designed nanophotonic devices offer promising solutions for analog optical computation. High-density photonic integration is critical for scaling such architectures toward more complex computational tasks and large-scale applications. Here, we present an inverse-designed photonic neural network (PNN) accelerator on a high-index contrast material platform, enabling ultra-compact and energy-efficient optical computing. Our approach introduces a wave-based inverse-design method based on th…

The Elastic Analysis Facility's (EAF's) Contribution to the Future of Analysis at Multi-Experiment Institutions and Future Colliders
Elise Chavez, Maria Acosta-Flechas, Christophe Bonnaud, Burt Holzman, Tulika Bose
https://arxiv.org/abs/2506.08222

The Elastic Analysis Facility's (EAF's) Contribution to the Future of Analysis at Multi-Experiment Institutions and Future Colliders
The Elastic Analysis Facility (EAF) hosted at Fermi National Accelerator Laboratory (Fermilab) is a platform being developed with the goal of providing a fast and efficient facility for physics analysis. As high-energy physics moves towards collecting larger datasets, such as those from the High-Luminosity LHC, the EAF strives to provide a powerful and adaptable framework for future colliders and multi-experiment institutions. Currently, the EAF supports several experiments including CMS, NOvA,…