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…
wa-hls4ml: A Benchmark and Surrogate Models for hls4ml Resource and Latency Estimation
Benjamin Hawks, Jason Weitz, Dmitri Demler +13
As machine learning (ML) is increasingly implemented in hardware to address real-time challenges in scientific applications, the development of advanced toolchains has significantl…
Building Machine Learning Challenges for Anomaly Detection in Science
Elizabeth G. Campolongo, Yuan-Tang Chou, Ekaterina Govorkova +148
Scientific discoveries are often made by finding a pattern or object that was not predicted by the known rules of science. Oftentimes, these anomalous events or objects that do not…
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…
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…