1 citations · 1 across the 4 of their papers we have counts for
5 papers
SNAC-Pack 2.0: Scaled-Out Surrogate Neural Architecture Codesign
Jason Weitz, Dmitri Demler, Benjamin Hawks +3
Neural architecture search (NAS) is a powerful approach for automating model design, but existing methods often optimize for accuracy alone or rely on proxy metrics such as bit ope…
Neural Architecture Codesign for Fast Physics Applications
Jason Weitz, Dmitri Demler, Luke McDermott +2
We develop a pipeline to streamline neural architecture codesign for physics applications to reduce the need for ML expertise when designing models for novel tasks. Our method empl…
Track reconstruction as a service for collider physics
Haoran Zhao, Yuan-Tang Chou, Yao Yao +11
Optimizing charged-particle track reconstruction algorithms is crucial for efficient event reconstruction in Large Hadron Collider (LHC) experiments due to their significant comput…
Architectural Implications of Neural Network Inference for High Data-Rate, Low-Latency Scientific Applications
Olivia Weng, Alexander Redding, Nhan Tran +2
With more scientific fields relying on neural networks (NNs) to process data incoming at extreme throughputs and latencies, it is crucial to develop NNs with all their parameters s…
Neural Architecture Codesign for Fast Bragg Peak Analysis
Luke McDermott, Jason Weitz, Dmitri Demler +3
We develop an automated pipeline to streamline neural architecture codesign for fast, real-time Bragg peak analysis in high-energy diffraction microscopy. Traditional approaches, n…