17 citations · 17 across the 6 of their papers we have counts for
6 papers
Benchmarking LLM Serving Systems for Agentic AI Workloads with XPerf
Michael Wang, Yikang Yue, Shaobo Li +3
We present XPerf, a benchmarking framework that load-tests LLM serving systems with diverse agentic AI workloads. It provides detailed profiling of the serving system and hardware,…
Exploring the Efficiency of 3D-Stacked AI Chip Architecture for LLM Inference with VOXEL
Yiqi Liu, Noelle Crawford, Michael Wang +2
To overcome the well-known memory bottleneck of AI chips, 3D-stacked architectures that employ advanced packaging technology with high-density through-silicon vias (TSVs) pins have…
Physics at the Edge: Benchmarking Quantisation Techniques and the Edge TPU for Neutrino Interaction Recognition
Stefano Vergani, Hilary Utaegbulam, Michael Wang +4
This work presents a benchmark of quantisation techniques for convolutional neural networks applied to neutrino interaction recognition, motivated by the need for low-power inferen…
Building an AI-native Research Ecosystem for Experimental Particle Physics: A Community Vision
Thea Klaeboe Aarrestad, Alaa Abdelhamid, Haider Abidi +457
Experimental particle physics seeks to understand the universe by probing its fundamental particles and forces and exploring how they govern the large-scale processes that shape co…
Machine Learning on Heterogeneous, Edge, and Quantum Hardware for Particle Physics (ML-HEQUPP)
Julia Gonski, Jennifer Ott, Jenni Ott +129
The next generation of particle physics experiments will face a new era of challenges in data acquisition, due to unprecedented data rates and volumes along with extreme environmen…
Using the Automata Processor for Fast Pattern Recognition in High Energy Physics Experiments - A Proof of Concept
Michael H. L. S. Wang, Gustavo Cancelo, Christopher Green +3
We explore the Micron Automata Processor (AP) as a suitable commodity technology that can address the growing computational needs of pattern recognition in High Energy Physics (HEP…